Generative organic electronic molecular design informed by quantum chemistry

Cheng-Han Li and Daniel P. Tabor

Chem. Sci., 2023, 14, 11045
DOI: 10.1039/D3SC03781A

Predicting reaction conditions from limited data through active transfer learning

Eunjae Shim, Joshua A. Kammeraad, Ziping Xu, Ambuj Tewari, Tim Cernak and Paul M. Zimmerman

Chem. Sci., 2022, 13, 6655
DOI: 10.1039/D1SC06932B

Meta optimization based on real-time benchmarking of multiple surrogate models for autonomous flow synthesis

Amirreza Mottafegh, Gwang-Noh Ahn and Dong-Pyo Kim

Lab Chip, 2023, 23, 1613
DOI: 10.1039/D2LC00938B

Bayesian optimization of nanoporous materials

Aryan Deshwal, Cory M. Simon and Janardhan Rao Doppa

Mol. Syst. Des. Eng., 2021, 6, 1066
DOI: 10.1039/D1ME00093D

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules

Francesco Gentile, Michael Fernandez, Fuqiang Ban, Anh-Tien Ton, Hazem Mslati, Carl F. Perez, Eric Leblanc, Jean Charle Yaacoub, James Gleave, Abraham Stern, Bill Wong, François Jean, Natalie Strynadka and Artem Cherkasov

Chem. Sci., 2021, 12, 15960
DOI: 10.1039/D1SC05579H

Graph-based networks for accurate prediction of ground and excited state molecular properties from minimal features

Denish Trivedi, Kalyani Patrikar and Anirban Mondal

Mol. Syst. Des. Eng., 2024, 9, 1275
DOI: 10.1039/D4ME00113C

Sample efficient reinforcement learning with active learning for molecular design

Michael Dodds, Jeff Guo, Thomas Löhr, Alessandro Tibo, Ola Engkvist and Jon Paul Janet

Chem. Sci., 2024, 15, 4146
DOI: 10.1039/D3SC04653B

PAL 2.0: a physics-driven bayesian optimization framework for material discovery

Maitreyee Sharma Priyadarshini, Oluwaseun Romiluyi, Yiran Wang, Kumar Miskin, Connor Ganley and Paulette Clancy

Mater. Horiz., 2024, 11, 781
DOI: 10.1039/D3MH01474F

PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening

Seonghwan Seo and Woo Youn Kim

Chem. Sci., 2024, 15, 19473
DOI: 10.1039/D4SC04854G

CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training

Heng Cai, Chao Shen, Tianye Jian, Xujun Zhang, Tong Chen, Xiaoqi Han, Zhuo Yang, Wei Dang, Chang-Yu Hsieh, Yu Kang, Peichen Pan, Xiangyang Ji, Jianfei Song, Tingjun Hou and Yafeng Deng

Chem. Sci., 2024, 15, 1449
DOI: 10.1039/D3SC05552C

Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS

Gary Tom, Riley J. Hickman, Aniket Zinzuwadia, Afshan Mohajeri, Benjamin Sanchez-Lengeling and Alán Aspuru-Guzik

Digital Discovery, 2023, 2, 759
DOI: 10.1039/D2DD00146B

Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation

Sheng Chen, Junjie Xie, Renlong Ye, David Daqiang Xu and Yuedong Yang

Chem. Sci., 2024, 15, 10366
DOI: 10.1039/D4SC00094C

Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease

Ben Cree, Mateusz K. Bieniek, Siddique Amin, Akane Kawamura and Daniel J. Cole

Digital Discovery, 2025, 4, 438
DOI: 10.1039/D4DD00343H

Spectra-descriptor-based machine learning for predicting protein–ligand interactions

Cheng Chen, Ledu Wang, Yi Feng, Wencheng Yao, Jiahe Liu, Zifan Jiang, Luyuan Zhao, Letian Zhang, Jun Jiang and Shuo Feng

Chem. Sci., 2025, 16, 6355
DOI: 10.1039/D5SC00451A

Retro-forward synthesis design and experimental validation of potent structural analogs of known drugs

Ahmad Makkawi, Wiktor Beker, Agnieszka Wołos, Sabyasachi Manna, Rafał Roszak, Sara Szymkuć, Martyna Moskal, Aleksei Koshevarnikov, Karol Molga, Anna Żądło-Dobrowolska and Bartosz A. Grzybowski

Chem. Sci., 2025, 16, 8383
DOI: 10.1039/D5SC00070J

Bayesian optimization with known experimental and design constraints for chemistry applications

Riley J. Hickman, Matteo Aldeghi, Florian Häse and Alán Aspuru-Guzik

Digital Discovery, 2022, 1, 732
DOI: 10.1039/D2DD00028H

Navigating the Maize: cyclic and conditional computational graphs for molecular simulation

Thomas Löhr, Michele Assante, Michael Dodds, Lili Cao, Mikhail Kabeshov, Jon-Paul Janet, Marco Klähn and Ola Engkvist

Digital Discovery, 2024, 3, 2551
DOI: 10.1039/D4DD00288A

Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties

Tsung-Wei Liu, Quan Nguyen, Adji Bousso Dieng and Diego A. Gómez-Gualdrón

Chem. Sci., 2024, 15, 18903
DOI: 10.1039/D4SC03609C

A diversity maximizing active learning strategy for graph neural network models of chemical properties

Bowen Li and Srinivas Rangarajan

Mol. Syst. Des. Eng., 2022, 7, 1697
DOI: 10.1039/D2ME00073C

Towards informatics-driven design of nuclear waste forms

Vinay I. Hegde, Miroslava Peterson, Sarah I. Allec, Xiaonan Lu, Thiruvillamalai Mahadevan, Thanh Nguyen, Jayani Kalahe, Jared Oshiro, Robert J. Seffens, Ethan K. Nickerson, Jincheng Du, Brian J. Riley, John D. Vienna and James E. Saal

Digital Discovery, 2024, 3, 1450
DOI: 10.1039/D4DD00096J

Pareto optimization to accelerate multi-objective virtual screening

Jenna C. Fromer, David E. Graff and Connor W. Coley

Digital Discovery, 2024, 3, 467
DOI: 10.1039/D3DD00227F

Uncertainty quantification: Can we trust artificial intelligence in drug discovery?

Jie Yu, Dingyan Wang and Mingyue Zheng

iScience, 2022, 25, 104814
DOI: 10.1016/j.isci.2022.104814

Scope of machine learning in materials research—A review

Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz and Md Israfil Hossain Rimon

Applied Surface Science Advances, 2023, 18, 100523
DOI: 10.1016/j.apsadv.2023.100523

Active learning strategies for the design of sustainable alloys

Ziyuan Rao, Anurag Bajpai and Hongbin Zhang

Phil. Trans. R. Soc. A., 2024, 382
DOI: 10.1098/rsta.2023.0242

Finite element analysis, machine learning, and digital twins for soft robots: state-of-arts and perspectives

Liuchao Jin, Xiaoya Zhai, Wenbo Xue, Kang Zhang, Jingchao Jiang, Mahdi Bodaghi and Wei-Hsin Liao

Smart Mater. Struct., 2025, 34, 033002
DOI: 10.1088/1361-665X/adadcd

Predicting high-fitness viral protein variants with Bayesian active learning and biophysics

Marian Huot, Dianzhuo Wang, Jiacheng Liu and Eugene I. Shakhnovich

Proc. Natl. Acad. Sci. U.S.A., 2025, 122
DOI: 10.1073/pnas.2503742122

Knowledge-aware design of high-strength aviation aluminum alloys via machine learning

Juan Yong-fei, Niu Guo-shuai, Yang Yang, Dai Yong-bing, Zhang Jiao, Han Yan-feng and Sun Bao-de

Journal of Materials Research and Technology, 2023, 24, 346
DOI: 10.1016/j.jmrt.2023.03.041

Automated On-the-Fly Optimization of Resource Allocation for Efficient Free Energy Simulations

S. Benjamin Koby, Evgeny Gutkin, Shree Patel and Maria G. Kurnikova

J. Chem. Inf. Model., 2025, 65, 4932
DOI: 10.1021/acs.jcim.4c02107

pyscreener: A Python Wrapper for Computational Docking Software

David E. Graff and Connor W. Coley

JOSS, 2022, 7, 3950
DOI: 10.21105/joss.03950

Discriminative Active Learning for Robotic Grasping in Cluttered Scene

Boyan Wei, Xianfeng Ye, Chengjiang Long, Zhenjun Du, Bangyu Li, Baocai Yin and Xin Yang

IEEE Robot. Autom. Lett., 2023, 8, 1858
DOI: 10.1109/LRA.2023.3243474

High-Performance Iridium–Molybdenum Oxide Electrocatalysts for Water Oxidation in Acid: Bayesian Optimization Discovery and Experimental Testing

Jacques A. Esterhuizen, Aarti Mathur, Bryan R. Goldsmith and Suljo Linic

J. Am. Chem. Soc., 2024, 146, 5511
DOI: 10.1021/jacs.3c13491

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Kwangho Nam, Yihan Shao, Dan T. Major and Magnus Wolf-Watz

ACS Omega, 2024
DOI: 10.1021/acsomega.3c09084

Active Learning FEP: Impact on Performance of AL Protocol and Chemical Diversity

Richard Lonsdale, Jack Glancy, Leen Kalash, David Marcus and Ian D. Wall

J. Chem. Theory Comput., 2025, 21, 4867
DOI: 10.1021/acs.jctc.5c00128

Computational Hit Finding: An Industry Perspective

Paraskevi Gkeka, Fredrik Svensson, Carlos Roca Magadán, Marcel John de Groot and Steven V. Jerome

J. Med. Chem., 2025, 68, 10507
DOI: 10.1021/acs.jmedchem.4c03087

Autoparty: Machine Learning-Guided Visual Inspection of Molecular Docking Results

Laura Shub, Magdalena Korczynska, Duncan F. Muir, Fang-Yu Lin, Brendan W. Hall, Alan M. Mathiowetz and Michael J. Keiser

J. Chem. Inf. Model., 2025
DOI: 10.1021/acs.jcim.5c00850

Effect of molecular representation on deep learning performance for prediction of molecular electronic properties

Jun Hyeong Kim, Hyeonsu Kim and Woo Youn Kim

Bulletin Korean Chem Soc, 2022, 43, 645
DOI: 10.1002/bkcs.12516

Computational approaches streamlining drug discovery

Anastasiia V. Sadybekov and Vsevolod Katritch

Nature, 2023, 616, 673
DOI: 10.1038/s41586-023-05905-z

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Alexander Tropsha, Olexandr Isayev, Alexandre Varnek, Gisbert Schneider and Artem Cherkasov

Nat Rev Drug Discov, 2024, 23, 141
DOI: 10.1038/s41573-023-00832-0

Target-driven machine learning-enabled virtual screening (TAME-VS) platform for early-stage hit identification

Yuemin Bian, Jason J. Kwon, Cong Liu, Enrico Margiotta, Mrinal Shekhar and Alexandra E. Gould

Front. Mol. Biosci., 2023, 10
DOI: 10.3389/fmolb.2023.1163536

Lighting and rapid detection of the Coronavirus S protein using computationally speculated ligand and its application in SARS-CoV-2

Yuan Liu, Xiaoyu Xie, Qingyuan Wang, Desheng Chen, Dehui Qiu, Xinrong Yan, Lichao Guo, Quanchi Chen, Xiaobo Zhang and Huangxian Ju

Sensors and Actuators B: Chemical, 2024, 418, 136284
DOI: 10.1016/j.snb.2024.136284

Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries

Toni Sivula, Laxman Yetukuri, Tuomo Kalliokoski, Heikki Käsnänen, Antti Poso and Ina Pöhner

J. Chem. Inf. Model., 2023, 63, 5773
DOI: 10.1021/acs.jcim.3c01239

Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations

Hannes H. Loeffler, Shunzhou Wan, Marco Klähn, Agastya P. Bhati and Peter V. Coveney

J. Chem. Theory Comput., 2024
DOI: 10.1021/acs.jctc.4c00576

It all clicks together: In silico drug discovery becoming mainstream

Antonina L. Nazarova and Vsevolod Katritch

Clinical & Translational Med, 2022, 12
DOI: 10.1002/ctm2.766

SpaceHASTEN: A Structure-Based Virtual Screening Tool for Nonenumerated Virtual Chemical Libraries

Tuomo Kalliokoski, Ainoleena Turku and Heikki Käsnänen

J. Chem. Inf. Model., 2025, 65, 125
DOI: 10.1021/acs.jcim.4c01790

Batched Bayesian Optimization for Drug Design in Noisy Environments

Hugo Bellamy, Abbi Abdel Rehim, Oghenejokpeme I. Orhobor and Ross King

J. Chem. Inf. Model., 2022, 62, 3970
DOI: 10.1021/acs.jcim.2c00602

Autonomous high-throughput screening technology for accelerating drug molecule discovery and synthesis

Jie Chen, Xian Zheng, Yixiang Ruan and Yiming Mo

Sci. Sin.-Chim, 2023, 53, 79
DOI: 10.1360/SSC-2022-0101

Multimodal Deep Representation Learning for Quantum Cross-Platform Verification

Yang Qian, Yuxuan Du, Zhenliang He, Min-Hsiu Hsieh and Dacheng Tao

Phys. Rev. Lett., 2024, 133
DOI: 10.1103/PhysRevLett.133.130601

Self-Focusing Virtual Screening with Active Design Space Pruning

David E. Graff, Matteo Aldeghi, Joseph A. Morrone, Kirk E. Jordan, Edward O. Pyzer-Knapp and Connor W. Coley

J. Chem. Inf. Model., 2022, 62, 3854
DOI: 10.1021/acs.jcim.2c00554

Yoked learning in molecular data science

Zhixiong Li, Yan Xiang, Yujing Wen and Daniel Reker

Artificial Intelligence in the Life Sciences, 2024, 5, 100089
DOI: 10.1016/j.ailsci.2023.100089

Assessing the Robustness and Scalability of Machine Learning Methods to Accelerate Ultralarge High-Throughput Docking Campaigns

Juan I. Di Filippo, Santiago Rómoli and Claudio N. Cavasotto

ACS Omega, 2025, 10, 15598
DOI: 10.1021/acsomega.5c00829

Synthon-based ligand discovery in virtual libraries of over 11 billion compounds

Arman A. Sadybekov, Anastasiia V. Sadybekov, Yongfeng Liu, Christos Iliopoulos-Tsoutsouvas, Xi-Ping Huang, Julie Pickett, Blake Houser, Nilkanth Patel, Ngan K. Tran, Fei Tong, Nikolai Zvonok, Manish K. Jain, Olena Savych, Dmytro S. Radchenko, Spyros P. Nikas, Nicos A. Petasis, Yurii S. Moroz, Bryan L. Roth, Alexandros Makriyannis and Vsevolod Katritch

Nature, 2022, 601, 452
DOI: 10.1038/s41586-021-04220-9

Coverage Score: A Model Agnostic Method to Efficiently Explore Chemical Space

Daniel J. Woodward, Anthony R. Bradley and Willem P. van Hoorn

J. Chem. Inf. Model., 2022, 62, 4391
DOI: 10.1021/acs.jcim.2c00258

The Art and Science of Molecular Docking

Joseph M. Paggi, Ayush Pandit and Ron O. Dror

Annual Review of Biochemistry, 2024, 93, 389
DOI: 10.1146/annurev-biochem-030222-120000

A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network

Yan Xiang, Yu-Hang Tang, Guang Lin and Huai Sun

J. Chem. Inf. Model., 2021, 61, 5414
DOI: 10.1021/acs.jcim.1c01118

The present state and challenges of active learning in drug discovery

Lei Wang, Zhenran Zhou, Xixi Yang, Shaohua Shi, Xiangxiang Zeng and Dongsheng Cao

Drug Discovery Today, 2024, 29, 103985
DOI: 10.1016/j.drudis.2024.103985

Inverse Design of Complex Block Copolymers for Exotic Self-Assembled Structures Based on Bayesian Optimization

Qingshu Dong, Xiangrui Gong, Kangrui Yuan, Ying Jiang, Liangshun Zhang and Weihua Li

ACS Macro Lett., 2023, 12, 401
DOI: 10.1021/acsmacrolett.3c00020

Accelerating Molecular Docking using Machine Learning Methods

Abdulsalam Y. Bande and Sefer Baday

Molecular Informatics, 2024, 43
DOI: 10.1002/minf.202300167

Advancing Rare-Earth (4f) and Actinide (5f) Separation through Machine Learning and Automated High-Throughput Experiments

Logan J. Augustine, Yufei Wang, Sara L. Adelman, Enrique R. Batista, Stosh A. Kozimor, Danny Perez, Joshua Schrier and Ping Yang

ACS Sustainable Chem. Eng., 2024, 12, 16692
DOI: 10.1021/acssuschemeng.4c06166

Efficient Exploration of Chemical Space with Docking and Deep Learning

Ying Yang, Kun Yao, Matthew P. Repasky, Karl Leswing, Robert Abel, Brian K. Shoichet and Steven V. Jerome

J. Chem. Theory Comput., 2021, 17, 7106
DOI: 10.1021/acs.jctc.1c00810

Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics

Kedar Hippalgaonkar, Qianxiao Li, Xiaonan Wang, John W. Fisher, James Kirkpatrick and Tonio Buonassisi

Nat Rev Mater, 2023, 8, 241
DOI: 10.1038/s41578-022-00513-1

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Davide Bassani and Stefano Moro

Molecules, 2023, 28, 3906
DOI: 10.3390/molecules28093906

Efficient design of peptide-binding polymers using active learning approaches

Assima Rakhimbekova, Anton Lopukhov, Natalia Klyachko, Alexander Kabanov, Timur I. Madzhidov and Alexander Tropsha

Journal of Controlled Release, 2023, 353, 903
DOI: 10.1016/j.jconrel.2022.11.023

Improving the Hit Rates of Virtual Screening by Active Learning from Bioactivity Feedback

Xun Deng, Junlong Liu, Zhike Liu, Jiansheng Wu, Fuli Feng, Jieping Ye and Zheng Wang

J. Chem. Theory Comput., 2025, 21, 4640
DOI: 10.1021/acs.jctc.4c01618

Artificial intelligence foundation for therapeutic science

Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun and Marinka Zitnik

Nat Chem Biol, 2022, 18, 1033
DOI: 10.1038/s41589-022-01131-2

A Bayesian active learning platform for scalable combination drug screens

Christopher Tosh, Mauricio Tec, Jessica B. White, Jeffrey F. Quinn, Glorymar Ibanez Sanchez, Paul Calder, Andrew L. Kung, Filemon S. Dela Cruz and Wesley Tansey

Nat Commun, 2025, 16
DOI: 10.1038/s41467-024-55287-7

Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains

Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III and Tonio Buonassisi

npj Comput Mater, 2021, 7
DOI: 10.1038/s41524-021-00656-9

Perspectives on current approaches to virtual screening in drug discovery

Ingo Muegge, Jörg Bentzien and Yunhui Ge

Expert Opinion on Drug Discovery, 2024, 19, 1173
DOI: 10.1080/17460441.2024.2390511

Enhancing preclinical drug discovery with artificial intelligence

R.S.K. Vijayan, Jan Kihlberg, Jason B. Cross and Vasanthanathan Poongavanam

Drug Discovery Today, 2022, 27, 967
DOI: 10.1016/j.drudis.2021.11.023

Uncertainty quantification with graph neural networks for efficient molecular design

Lung-Yi Chen and Yi-Pei Li

Nat Commun, 2025, 16
DOI: 10.1038/s41467-025-58503-0

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Xiliang Yan, Tongtao Yue, David A. Winkler, Yongguang Yin, Hao Zhu, Guibin Jiang and Bing Yan

Chem. Rev., 2023, 123, 8575
DOI: 10.1021/acs.chemrev.3c00070

Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study

Lin Wei, Min Xu, Zhiqiang Liu, Chongguo Jiang, Xiaohua Lin, Yaogang Hu, Xiaoming Wen, Rongfeng Zou, Chunwang Peng, Hongrui Lin, Guo Wang, Lijun Yang, Lei Fang, Mingjun Yang and Peiyu Zhang

J. Chem. Inf. Model., 2023, 63, 5341
DOI: 10.1021/acs.jcim.3c00543

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Simon Viet Johansson, Hampus Gummesson Svensson, Esben Bjerrum, Alexander Schliep, Morteza Haghir Chehreghani, Christian Tyrchan and Ola Engkvist

Molecular Informatics, 2022, 41
DOI: 10.1002/minf.202200043

Active Learning Guided Discovery of High Entropy Oxides Featuring High H2-production

Siyang Nie, Yan Xiang, Liang Wu, Guang Lin, Qingda Liu, Shengqi Chu and Xun Wang

J. Am. Chem. Soc., 2024, 146, 29325
DOI: 10.1021/jacs.4c06272

Viral proteases as therapeutic targets

Taťána Majerová and Jan Konvalinka

Molecular Aspects of Medicine, 2022, 88, 101159
DOI: 10.1016/j.mam.2022.101159

MBC and ECBL libraries: outstanding tools for drug discovery

Tiziana Ginex, Enrique Madruga, Ana Martinez and Carmen Gil

Front. Pharmacol., 2023, 14
DOI: 10.3389/fphar.2023.1244317

Exploration on learning molecular docking with deep learning models

Qin Xie, Wei Ma, Jianhang Zhang, Shiliang Li, Xiaobing Deng, Youjun Xu and Weilin Zhang

Quant. Biol., 2023, 11, 320
DOI: 10.15302/J-QB-022-0321

Data-Driven Design and Autonomous Experimentation in Soft and Biological Materials Engineering

Andrew L. Ferguson and Keith A. Brown

Annu. Rev. Chem. Biomol. Eng., 2022, 13, 25
DOI: 10.1146/annurev-chembioeng-092120-020803

Synthon-Based Strategies Exploiting Molecular Similarity and Protein–Ligand Interactions for Efficient Screening of Ultra-Large Chemical Libraries

Brian Medel-Lacruz, Albert Herrero, Fernando Martín, Enric Herrero, F. Javier Luque and Javier Vázquez

J. Chem. Inf. Model., 2025, 65, 7569
DOI: 10.1021/acs.jcim.5c00222

Integrating AI into OLED material design: a comprehensive review of computational frameworks, challenges, and opportunities

Yiming Shi, Ming Sun, Haochen Shi, Zhiqin Liang, Bo Qiao, Suling Zhao, Xuemei Pu and Dandan Song

Science Bulletin, 2025
DOI: 10.1016/j.scib.2025.07.007

Maximizing the integration of virtual and experimental screening in hit discovery

Dávid Bajusz and György M Keserű

Expert Opinion on Drug Discovery, 2022, 17, 629
DOI: 10.1080/17460441.2022.2085685

Accurate property prediction with interpretable machine learning model for small datasets via transformed atom vector

Xinyu Chen, Shuaihua Lu, Xinyang Wan, Qian Chen, Qionghua Zhou and Jinlan Wang

Phys. Rev. Materials, 2022, 6
DOI: 10.1103/PhysRevMaterials.6.123803

Compactness matters: Improving Bayesian optimization efficiency of materials formulations through invariant search spaces

Sterling G. Baird, Jason R. Hall and Taylor D. Sparks

Computational Materials Science, 2023, 224, 112134
DOI: 10.1016/j.commatsci.2023.112134

Force field-inspired transformer network assisted crystal density prediction for energetic materials

Jun-Xuan Jin, Gao-Peng Ren, Jianjian Hu, Yingzhe Liu, Yunhu Gao, Ke-Jun Wu and Yuchen He

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00736-6

Plant Antimicrobial Peptides as Potential Tool for Topic Treatment of Hidradenitis Suppurativa

Carlos André dos Santos-Silva, Paola Maura Tricarico, Lívia Maria Batista Vilela, Ricardo Salas Roldan-Filho, Vinícius Costa Amador, Adamo Pio d’Adamo, Mireli de Santana Rêgo, Ana Maria Benko-Iseppon and Sergio Crovella

Front. Microbiol., 2021, 12
DOI: 10.3389/fmicb.2021.795217

Self-Driving Laboratories for Chemistry and Materials Science

Gary Tom, Stefan P. Schmid, Sterling G. Baird, Yang Cao, Kourosh Darvish, Han Hao, Stanley Lo, Sergio Pablo-García, Ella M. Rajaonson, Marta Skreta, Naruki Yoshikawa, Samantha Corapi, Gun Deniz Akkoc, Felix Strieth-Kalthoff, Martin Seifrid and Alán Aspuru-Guzik

Chem. Rev., 2024, 124, 9633
DOI: 10.1021/acs.chemrev.4c00055

MO-MEMES: A method for accelerating virtual screening using multi-objective Bayesian optimization

Sarvesh Mehta, Manan Goel and U. Deva Priyakumar

Front. Med., 2022, 9
DOI: 10.3389/fmed.2022.916481

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries

Moayad Alnammi, Shengchao Liu, Spencer S. Ericksen, Gene E. Ananiev, Andrew F. Voter, Song Guo, James L. Keck, F. Michael Hoffmann, Scott A. Wildman and Anthony Gitter

J. Chem. Inf. Model., 2023, 63, 5513
DOI: 10.1021/acs.jcim.3c00912

Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor

Katarina Elez, Tim Hempel, Jonathan H. Shrimp, Nicole Moor, Lluís Raich, Cheila Rocha, Robin Winter, Tuan Le, Stefan Pöhlmann, Markus Hoffmann, Matthew D. Hall and Frank Noé

Nat Commun, 2025, 16
DOI: 10.1038/s41467-025-62139-5

Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1

Anantha Krishnan Dhanabalan, Velmurugan Devadasan, Jebiti Haribabu and Gunasekaran Krishnasamy

Mol Divers, 2025, 29, 2945
DOI: 10.1007/s11030-024-10997-4

A Review on Parallel Virtual Screening Softwares for High-Performance Computers

Natarajan Arul Murugan, Artur Podobas, Davide Gadioli, Emanuele Vitali, Gianluca Palermo and Stefano Markidis

Pharmaceuticals, 2022, 15, 63
DOI: 10.3390/ph15010063

Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

Roshan A. Patel and Michael A. Webb

ACS Appl. Bio Mater., 2024, 7, 510
DOI: 10.1021/acsabm.2c00962

Scientific discovery in the age of artificial intelligence

Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Veličković, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio and Marinka Zitnik

Nature, 2023, 620, 47
DOI: 10.1038/s41586-023-06221-2

Science‐Driven Atomistic Machine Learning

Johannes T. Margraf

Angew Chem Int Ed, 2023, 62
DOI: 10.1002/anie.202219170

FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools

Vishwesh Venkatraman

Front. Chem., 2023, 11
DOI: 10.3389/fchem.2023.1239467

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets

Maria H. Rasmussen, Chenru Duan, Heather J. Kulik and Jan H. Jensen

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00790-0

Efficient and enhanced sampling of drug‐like chemical space for virtual screening and molecular design using modern machine learning methods

Manan Goel, Rishal Aggarwal, Bhuvanesh Sridharan, Pradeep Kumar Pal and U. Deva Priyakumar

WIREs Comput Mol Sci, 2023, 13
DOI: 10.1002/wcms.1637

El Agente: An autonomous agent for quantum chemistry

Yunheng Zou, Austin H. Cheng, Abdulrahman Aldossary, Jiaru Bai, Shi Xuan Leong, Jorge Arturo Campos-Gonzalez-Angulo, Changhyeok Choi, Cher Tian Ser, Gary Tom, Andrew Wang, Zijian Zhang, Ilya Yakavets, Han Hao, Chris Crebolder, Varinia Bernales and Alán Aspuru-Guzik

Matter, 2025, 8, 102263
DOI: 10.1016/j.matt.2025.102263

A bottom-up approach to find lead compounds in expansive chemical spaces

Álvaro Serrano-Morrás, Andrea Bertran-Mostazo, Marina Miñarro-Lleonar, Arnau Comajuncosa-Creus, Adrià Cabello, Carme Labranya, Carmen Escudero, Tian V. Tian, Inna Khutorianska, Dmytro S. Radchenko, Yurii S. Moroz, Lucas Defelipe, David Ruiz-Carrillo, Maria Garcia-Alai, Robert Schmidt, Matthias Rarey, Patrick Aloy, Carles Galdeano, Jordi Juárez-Jiménez and Xavier Barril

Commun Chem, 2025, 8
DOI: 10.1038/s42004-025-01610-2

Computational methods to assist in the discovery of pharmacological chaperones for rare diseases

Bernardina Scafuri, Anna Verdino, Nancy D'Arminio and Anna Marabotti

Briefings in Bioinformatics, 2022, 23
DOI: 10.1093/bib/bbac198

The transformational role of GPU computing and deep learning in drug discovery

Mohit Pandey, Michael Fernandez, Francesco Gentile, Olexandr Isayev, Alexander Tropsha, Abraham C. Stern and Artem Cherkasov

Nat Mach Intell, 2022, 4, 211
DOI: 10.1038/s42256-022-00463-x

Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors

Dong Wang, Zhenxing Wu, Chao Shen, Lingjie Bao, Hao Luo, Zhe Wang, Hucheng Yao, De-Xin Kong, Cheng Luo and Tingjun Hou

Briefings in Bioinformatics, 2023, 24
DOI: 10.1093/bib/bbac592

MOFs to Enhance Green NH3 Synthesis in Plasma Reactors: Hierarchical Computational Screening Enhanced by Iterative Machine Learning

Tsung-Wei Liu, Fernando Fajardo-Rojas, Sumaya Addish, Enrique Martinez and Diego A. Gomez-Gualdron

ACS Appl. Mater. Interfaces, 2024, 16, 68506
DOI: 10.1021/acsami.4c11396

Keeping pace with the explosive growth of chemical libraries with structure‐based virtual screening

Jacqueline Kuan, Mariia Radaeva, Adeline Avenido, Artem Cherkasov and Francesco Gentile

WIREs Comput Mol Sci, 2023, 13
DOI: 10.1002/wcms.1678

Cost-Aware Bayesian Optimization for Self-Driven Condition Screening of Flow Electrosynthesis

Runzhe Liang, Siyuan Zheng, Kai Wang and Zhihong Yuan

ACS Electrochem., 2025, 1, 360
DOI: 10.1021/acselectrochem.4c00078

Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up

Rishi E. Kumar, Armi Tiihonen, Shijing Sun, David P. Fenning, Zhe Liu and Tonio Buonassisi

Matter, 2022, 5, 1353
DOI: 10.1016/j.matt.2022.04.016

Advancing plant biology through deep learning-powered natural language processing

Shuang Peng and Loïc Rajjou

Plant Cell Rep, 2024, 43
DOI: 10.1007/s00299-024-03294-9

Thompson Sampling─An Efficient Method for Searching Ultralarge Synthesis on Demand Databases

Kathryn Klarich, Brian Goldman, Trevor Kramer, Patrick Riley and W. Patrick Walters

J. Chem. Inf. Model., 2024, 64, 1158
DOI: 10.1021/acs.jcim.3c01790

Equivariant graph neural network-based accurate and ultra-fast virtual screening of small molecules targeting miRNA-protein complex

Huabei Wang, Zhimin Zhang, Guangyang Zhang, Ming Wen and Hongmei Lu

Journal of Pharmaceutical Analysis, 2025, 101339
DOI: 10.1016/j.jpha.2025.101339

Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines

Karthik Viswanathan, Manan Goel, Siddhartha Laghuvarapu, Girish Varma and U. Deva Priyakumar

Sci Rep, 2023, 13
DOI: 10.1038/s41598-023-42952-y

High-Temperature Catalytic Platform Powered by Thermophilic Microorganisms and Thermozymes

Jiawei Li, Lichao Sun and Yi-Xin Huo

Synthetic Biology and Engineering, 2025, 3, 10001
DOI: 10.70322/sbe.2025.10001

Self-Driving Synthesis of Protein Nanoparticles by Active Transfer-Learning-Assisted Autonomous Flow Platform

Amirreza Mottafegh, Jianwen Yang, Gi-Su Na and Dong-Pyo Kim

ACS Sustainable Chem. Eng., 2024, 12, 13185
DOI: 10.1021/acssuschemeng.4c03592

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking

Claudio N. Cavasotto and Juan I. Di Filippo

J. Chem. Inf. Model., 2023, 63, 2267
DOI: 10.1021/acs.jcim.2c01471

Lessons learnt from machine learning in early stages of drug discovery

Claudio N. Cavasotto, Juan I. Di Filippo and Valeria Scardino

Expert Opinion on Drug Discovery, 2024, 19, 631
DOI: 10.1080/17460441.2024.2354279

Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery

Susannah L. Parkhill and Eachan O. Johnson

Biochemical Journal, 2024, 481, 839
DOI: 10.1042/BCJ20220062

Integrating Machine Learning-Based Pose Sampling with Established Scoring Functions for Virtual Screening

Thi Ngoc Lan Vu, Hosein Fooladi and Johannes Kirchmair

J. Chem. Inf. Model., 2025, 65, 4833
DOI: 10.1021/acs.jcim.5c00380

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening

Xujun Zhang, Chao Shen, Haotian Zhang, Yu Kang, Chang-Yu Hsieh and Tingjun Hou

Acc. Chem. Res., 2024, 57, 1500
DOI: 10.1021/acs.accounts.4c00093

A Database for Large-Scale Docking and Experimental Results

Brendan W. Hall, Tia A. Tummino, Khanh Tang, Olivier Mailhot, Mar Castanon, John J. Irwin and Brian K. Shoichet

J. Chem. Inf. Model., 2025, 65, 4458
DOI: 10.1021/acs.jcim.5c00394

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design

Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender and Sergio Bacallado

J. Chem. Inf. Model., 2022, 62, 3486
DOI: 10.1021/acs.jcim.1c01334

De novo drug design through artificial intelligence: an introduction

Davide Crucitti, Carlos Pérez Míguez, José Ángel Díaz Arias, Diego Beltrán Fernandez Prada and Adrián Mosquera Orgueira

Front. Hematol., 2024, 3
DOI: 10.3389/frhem.2024.1305741

Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking

Egor Marin, Margarita Kovaleva, Maria Kadukova, Khalid Mustafin, Polina Khorn, Andrey Rogachev, Alexey Mishin, Albert Guskov and Valentin Borshchevskiy

J. Chem. Inf. Model., 2024, 64, 2612
DOI: 10.1021/acs.jcim.3c01661

Data-centric challenges with the application and adoption of artificial intelligence for drug discovery

Ghita Ghislat, Saiveth Hernandez-Hernandez, Chayanit Piyawajanusorn and Pedro J. Ballester

Expert Opinion on Drug Discovery, 2024, 19, 1297
DOI: 10.1080/17460441.2024.2403639

Small Molecules Targeting the Structural Dynamics of AR-V7 Partially Disordered Proteins Using Deep Ensemble Docking

Pantelis Karatzas, Z. Faidon Brotzakis and Haralambos Sarimveis

J. Chem. Theory Comput., 2025, 21, 4898
DOI: 10.1021/acs.jctc.5c00171

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery

Nicholas Casetti, Javier E. Alfonso‐Ramos, Connor W. Coley and Thijs Stuyver

Chemistry A European J, 2023, 29
DOI: 10.1002/chem.202301957

Efficient Discovery of Visible Light-Activated Azoarene Photoswitches with Long Half-Lives Using Active Search

Fatemah Mukadum, Quan Nguyen, Daniel M. Adrion, Gabriel Appleby, Rui Chen, Haley Dang, Remco Chang, Roman Garnett and Steven A. Lopez

J. Chem. Inf. Model., 2021, 61, 5524
DOI: 10.1021/acs.jcim.1c00954

Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors

Periyasamy Natarajan Shiammala, Navaneetha Krishna Bose Duraimutharasan, Baskaralingam Vaseeharan, Abdulaziz S. Alothaim, Esam S. Al-Malki, Babu Snekaa, Sher Zaman Safi, Sanjeev Kumar Singh, Devadasan Velmurugan and Chandrabose Selvaraj

Methods, 2023, 219, 82
DOI: 10.1016/j.ymeth.2023.09.010

Guided structure-based ligand identification and design via artificial intelligence modeling

Juan I. Di Filippo and Claudio N. Cavasotto

Expert Opinion on Drug Discovery, 2022, 17, 71
DOI: 10.1080/17460441.2021.1979514

Structure-based discovery of novel P-glycoprotein inhibitors targeting the nucleotide binding domains

Laust Moesgaard, Maria L. Pedersen, Carsten Uhd Nielsen and Jacob Kongsted

Sci Rep, 2023, 13
DOI: 10.1038/s41598-023-48281-4

Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking

Francesco Gentile, Jean Charle Yaacoub, James Gleave, Michael Fernandez, Anh-Tien Ton, Fuqiang Ban, Abraham Stern and Artem Cherkasov

Nat Protoc, 2022, 17, 672
DOI: 10.1038/s41596-021-00659-2

Active learning approaches in molecule pKi prediction

I. M. Kashafutdinova, A. Poyezzhayeva, T. Gimadiev and T. Madzhidov

Molecular Informatics, 2025, 44
DOI: 10.1002/minf.202400154

Traversing chemical space with active deep learning for low-data drug discovery

Derek van Tilborg and Francesca Grisoni

Nat Comput Sci, 2024, 4, 786
DOI: 10.1038/s43588-024-00697-2

Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces

Chen Cheng and Paul Beroza

J. Chem. Inf. Model., 2024, 64, 1251
DOI: 10.1021/acs.jcim.3c01865

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation

Gregory W. Kyro, Anton Morgunov, Rafael I. Brent and Victor S. Batista

J. Chem. Inf. Model., 2024, 64, 653
DOI: 10.1021/acs.jcim.3c01456

Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum

Jenna Fromer, Runzhong Wang, Mrunali Manjrekar, Austin Tripp, José Miguel Hernández-Lobato and Connor W. Coley

J. Chem. Inf. Model., 2025, 65, 4808
DOI: 10.1021/acs.jcim.5c00214

Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Shiyi Qin, Surajudeen Omolabake, Aminata Diaby, Jianping Li, Leonardo D. González, Christopher M. Holland, Victor M. Zavala, Shannon S. Stahl and Reid C. Van Lehn

ACS Sustainable Chem. Eng., 2024, 12, 18634
DOI: 10.1021/acssuschemeng.4c07423

An artificial intelligence accelerated virtual screening platform for drug discovery

Guangfeng Zhou, Domnita-Valeria Rusnac, Hahnbeom Park, Daniele Canzani, Hai Minh Nguyen, Lance Stewart, Matthew F. Bush, Phuong Tran Nguyen, Heike Wulff, Vladimir Yarov-Yarovoy, Ning Zheng and Frank DiMaio

Nat Commun, 2024, 15
DOI: 10.1038/s41467-024-52061-7

A Deep-Learning Proteomic-Scale Approach for Drug Design

Brennan Overhoff, Zackary Falls, William Mangione and Ram Samudrala

Pharmaceuticals, 2021, 14, 1277
DOI: 10.3390/ph14121277

Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules

Yan Xiang, Yu-Hang Tang, Zheng Gong, Hongyi Liu, Liang Wu, Guang Lin and Huai Sun

J. Chem. Inf. Model., 2023, 63, 6515
DOI: 10.1021/acs.jcim.3c01430

Accelerating BRPF1b hit identification with BioPhysical and Active Learning Screening (BioPALS)

Sandeep Pal, Zandile Nare, Vincenzo A. Rao, Brian O. Smith, Ian Morrison, Edward A. Fitzgerald, Andrew Scott, Matilda J. Bingham and Thomas Pesnot

ChemMedChem, 2024, 19
DOI: 10.1002/cmdc.202300590

Neural Network-Based Molecular Dynamics Simulation of Water Assisted by Active Learning

Dan Zhao, Yao Huang and Hujun Shen

J. Phys. Chem. B, 2025, 129, 3829
DOI: 10.1021/acs.jpcb.4c06633

A Simple Way to Incorporate Target Structural Information in Molecular Generative Models

Wenyi Zhang, Kaiyue Zhang and Jing Huang

J. Chem. Inf. Model., 2023, 63, 3719
DOI: 10.1021/acs.jcim.3c00293

Machine Learning Guided AQFEP: A Fast and Efficient Absolute Free Energy Perturbation Solution for Virtual Screening

Jordan E. Crivelli-Decker, Zane Beckwith, Gary Tom, Ly Le, Sheenam Khuttan, Romelia Salomon-Ferrer, Jackson Beall, Rafael Gómez-Bombarelli and Andrea Bortolato

J. Chem. Theory Comput., 2024
DOI: 10.1021/acs.jctc.4c00399

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

Md Ataul Islam, Dawood Babu Dudekula, V. P. Subramanyam Rallabandi, Sridhar Srinivasan, Sathishkumar Natarajan, Hoyong Chung and Junhyung Park

IJMS, 2022, 23, 9374
DOI: 10.3390/ijms23169374

Caught between a ROCK and a hard place: current challenges in structure-based drug design

Daniele Pala and David E. Clark

Drug Discovery Today, 2024, 29, 104106
DOI: 10.1016/j.drudis.2024.104106

Bayesian illumination: Inference and quality-diversity accelerate generative molecular models

Jonas Verhellen

SciPost Chem., 2025, 4
DOI: 10.21468/SciPostChem.4.1.001

AI meets physics in computational structure-based drug discovery for GPCRs

Mayako Michino, Jeremie Vendome and Irina Kufareva

npj Drug Discov., 2025, 2
DOI: 10.1038/s44386-025-00019-0

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

Amit Gangwal, Azim Ansari, Iqrar Ahmad, Abul Kalam Azad, Vinoth Kumarasamy, Vetriselvan Subramaniyan and Ling Shing Wong

Front. Pharmacol., 2024, 15
DOI: 10.3389/fphar.2024.1331062

Enhanced Machine Learning Molecular Simulations for optimization of flotation selectivity: A perspective paper

D. Dell’Angelo, Y. Foucaud, J. Mesquita, J. Lainé, H. Turrer and M. Badawi

Minerals Engineering, 2024, 218, 109016
DOI: 10.1016/j.mineng.2024.109016

Artificial Intelligence in Virtual Screening: Transforming Drug Research and Discovery—A Review

Sayantani Roy, Karuppiah Nagaraj, Amit Mittal, Flora C. Shah and Kaliyaperumal Raja

J Bio-X Res, 2025, 8
DOI: 10.34133/jbioxresearch.0041

Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction

Rohan Gorantla, Alžbeta Kubincová, Benjamin Suutari, Benjamin P. Cossins and Antonia S. J. S. Mey

J. Chem. Inf. Model., 2024, 64, 1955
DOI: 10.1021/acs.jcim.4c00220

Evolutionary Multiobjective Molecule Optimization in an Implicit Chemical Space

Xin Xia, Yiping Liu, Chunhou Zheng, Xingyi Zhang, Qingwen Wu, Xin Gao, Xiangxiang Zeng and Yansen Su

J. Chem. Inf. Model., 2024, 64, 5161
DOI: 10.1021/acs.jcim.4c00031

Science‐Driven Atomistic Machine Learning

Johannes T. Margraf

Angewandte Chemie, 2023, 135
DOI: 10.1002/ange.202219170

STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning

Likun Yang, Xiaoli Liang, Na Zhang and Lu Lu

ACS Omega, 2023, 8, 44751
DOI: 10.1021/acsomega.3c04832

Ultra‐Large Virtual Screening: Definition, Recent Advances, and Challenges in Drug Design

Gabriel Corrêa Veríssimo, Rafaela Salgado Ferreira and Vinícius Gonçalves Maltarollo

Molecular Informatics, 2025, 44
DOI: 10.1002/minf.202400305

Kinase Drug Discovery: An Artificial Intelligence Revolution

Trapper Johnson, Nisha Tulshan and Theodore Lemuel Mathuram

AI in Precision Oncology, 2025, 2, 52
DOI: 10.1089/aipo.2025.0002

Artificial intelligence in molecular de novo design: Integration with experiment

Jon Paul Janet, Lewis Mervin and Ola Engkvist

Current Opinion in Structural Biology, 2023, 80, 102575
DOI: 10.1016/j.sbi.2023.102575

Advances in machine intelligence‐driven virtual screening approaches for big‐data

Neeraj Kumar and Vishal Acharya

Medicinal Research Reviews, 2024, 44, 939
DOI: 10.1002/med.21995

Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds

Kai Zhang and Huichun Zhang

ACS EST Water, 2024, 4, 773
DOI: 10.1021/acsestwater.2c00193

Active Learning of Ligands That Enhance Perovskite Nanocrystal Luminescence

Min A. Kim, Qianxiang Ai, Alexander J. Norquist, Joshua Schrier and Emory M. Chan

ACS Nano, 2024, 18, 14514
DOI: 10.1021/acsnano.4c02094

Structure-based virtual screening of vast chemical space as a starting point for drug discovery

Jens Carlsson and Andreas Luttens

Current Opinion in Structural Biology, 2024, 87, 102829
DOI: 10.1016/j.sbi.2024.102829

Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds

Brendan W. Hall and Michael J. Keiser

J. Chem. Inf. Model., 2024, 64, 7398
DOI: 10.1021/acs.jcim.4c00683

Applying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development

Xindong Chen, Shihao Xu, Bizhu Chu, Jing Guo, Huikai Zhang, Shuyi Sun, Le Song and Xi-Qiao Feng

ACS Nano, 2024, 18, 29311
DOI: 10.1021/acsnano.4c12599

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Yunchao Xie, Kianoosh Sattari, Chi Zhang and Jian Lin

Progress in Materials Science, 2023, 132, 101043
DOI: 10.1016/j.pmatsci.2022.101043

Bayesian data-driven models for pharmaceutical process development

Hochan Chang, Nathan Domagalski, Jose E Tabora and Jean W Tom

Current Opinion in Chemical Engineering, 2024, 45, 101034
DOI: 10.1016/j.coche.2024.101034

Machine learning directed multi-objective optimization of mixed variable chemical systems

Oliver J. Kershaw, Adam D. Clayton, Jamie A. Manson, Alexandre Barthelme, John Pavey, Philip Peach, Jason Mustakis, Roger M. Howard, Thomas W. Chamberlain, Nicholas J. Warren and Richard A. Bourne

Chemical Engineering Journal, 2023, 451, 138443
DOI: 10.1016/j.cej.2022.138443

Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology

Daniela Grasso, Silvia Galderisi, Annalisa Santucci and Andrea Bernini

IJMS, 2023, 24, 5819
DOI: 10.3390/ijms24065819

Emerging structure-based computational methods to screen the exploding accessible chemical space

Corentin Bedart, Conrad Veranso Simoben and Matthieu Schapira

Current Opinion in Structural Biology, 2024, 86, 102812
DOI: 10.1016/j.sbi.2024.102812

Real-World Applications and Experiences of AI/ML Deployment for Drug Discovery

Will R. Pitt, Jonathan Bentley, Christophe Boldron, Lionel Colliandre, Carmen Esposito, Elizabeth H. Frush, Jola Kopec, Stéphanie Labouille, Jerome Meneyrol, David A. Pardoe, Ferruccio Palazzesi, Alfonso Pozzan, Jacob M. Remington, René Rex, Michelle Southey, Sachin Vishwakarma and Paul Walker

J. Med. Chem., 2025, 68, 851
DOI: 10.1021/acs.jmedchem.4c03044

Active Learning to Select the Most Suitable Reagents and One-Step Organic Chemistry Reactions for Prioritizing Target-Specific Hits from Ultralarge Chemical Spaces

Vladimir Kozyrev, François Sindt and Didier Rognan

J. Chem. Inf. Model., 2025, 65, 693
DOI: 10.1021/acs.jcim.4c02097

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening

Zhonglin Cao, Simone Sciabola and Ye Wang

J. Chem. Inf. Model., 2024, 64, 1882
DOI: 10.1021/acs.jcim.3c01938

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches

Christoph Gorgulla

Annu. Rev. Biomed. Data Sci., 2023, 6, 229
DOI: 10.1146/annurev-biodatasci-020222-025013

Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer’s disease

Feixiong Cheng, Fei Wang, Jian Tang, Yadi Zhou, Zhimin Fu, Pengyue Zhang, Jonathan L. Haines, James B. Leverenz, Li Gan, Jianying Hu, Michal Rosen-Zvi, Andrew A. Pieper and Jeffrey Cummings

Cell Reports Medicine, 2024, 5, 101379
DOI: 10.1016/j.xcrm.2023.101379

Industrializing AI/ML during the end-to-end drug discovery process

Jiho Yoo, Tae Yong Kim, InSuk Joung and Sang Ok Song

Current Opinion in Structural Biology, 2023, 79, 102528
DOI: 10.1016/j.sbi.2023.102528

Artificial intelligence for drug repurposing against infectious diseases

Anuradha Singh

Artificial Intelligence Chemistry, 2024, 2, 100071
DOI: 10.1016/j.aichem.2024.100071