Themed collection AI in Medicinal Chemistry
AiZynth impact on medicinal chemistry practice at AstraZeneca
The AI retrosynthesis tool AiZynth has made positive impacts on AstraZeneca drug discovery projects. This opinion provides some examples and discusses how AI retrosynthesis fits into pharmaceutical research.
RSC Med. Chem., 2024,15, 1085-1095
https://doi.org/10.1039/D3MD00651D
Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry
RSC Med. Chem., 2024, Accepted Manuscript
https://doi.org/10.1039/D4MD00196F
Leveraging bounded datapoints to classify molecular potency improvements
We present a novel data pre-processing approach, “DeltaClassifier”, that enables classification models to access traditionally inaccessible bounded datapoints to guide molecular optimizations by directly contrasting pairs of molecules.
RSC Med. Chem., 2024, Advance Article
https://doi.org/10.1039/D4MD00325J
Identification of Lysosomotropism using Explainable Machine Learning and Morphological Profiling Cell Painting Data
RSC Med. Chem., 2024, Accepted Manuscript
https://doi.org/10.1039/D4MD00107A
Systematic generation and analysis of counterfactuals for compound activity predictions using multi-task models
For a kinase inhibitor correctly predicted with a multi-task machine learning model (shown on an orange background), counterfactuals with small chemical changes (shown in red) were generated that were predicted to be active against other kinases.
RSC Med. Chem., 2024,15, 1547-1555
https://doi.org/10.1039/D4MD00128A
Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches
A pragmatic approach to the discovery of new SARS-COV-2 Mpro inhibitors by combining generative chemistry and computational chemistry approaches.
RSC Med. Chem., 2024, Advance Article
https://doi.org/10.1039/D4MD00106K
Can large language models predict antimicrobial peptide activity and toxicity?
The large language models GPT-3 and GTP-3.5 were challenged to predict the activity and hemolysis of antimicrobial peptides from their sequence and compared to recurrent neural networks and support vector machines.
RSC Med. Chem., 2024, Advance Article
https://doi.org/10.1039/D4MD00159A
Discovery of novel SOS1 inhibitors using machine learning
Machine learning enabled ligand-based virtual screening is a valuable tool in discovering effective SOS1 inhibitors.
RSC Med. Chem., 2024,15, 1392-1403
https://doi.org/10.1039/D4MD00063C
Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning
Augmenting direct-to-biology workflows with a new machine learning framework.
RSC Med. Chem., 2024,15, 1015-1021
https://doi.org/10.1039/D3MD00719G
About this collection
This themed collection, guest edited by Professor Jian Zhang (Shanghai Jiao Tong), Professor Ola Engkvist (Astrazeneca) and Dr Gerhard Hessler (Sanofi), highlights the latest advances in the field of artificial intelligence applied to medicinal chemistry. The field of AI has progressed very fast during the last decade. The progress has impacted many fields including drug discovery and development. This collection covers various areas related to the application of AI for designing novel compounds of medical relevance as well as using AI for designing the synthetic routes for medicinal chemistry relevant compounds. Areas of how the design of compounds can be combined through AI with automation, information rich assays like image & transcriptomics assays and large language models like GTP4 will also be included.
New articles will be added to the collection upon publication. Please return to this page frequently to see the collection grow.