The way to AI-controlled synthesis: how far do we need to go?

Chemical synthesis always plays an irreplaceable role in chemical, materials, and pharmacological fields. Meanwhile, artificial intelligence (AI) is causing a rapid technological revolution in many fields by replacing manual chemical synthesis and has exhibited a much more economical and time-efficient manner. However, the rate-determining step of AI-controlled synthesis systems is rarely mentioned, which makes it difficult to apply them in general laboratories. Here, the history of developing AI-aided synthesis has been overviewed and summarized. We propose that the hardware of AI-controlled synthesis systems should be more adaptive to execute reactions with different phase reagents and under different reaction conditions, and the software of AI-controlled synthesis systems should have richer kinds of reaction prediction modules. An updated system will better address more different kinds of syntheses. Our viewpoint could help scientists advance the revolution that combines AI and synthesis to achieve more progress in complicated systems.


Introduction
Aer decades of pioneering research in academia, it has been shown that chemical synthesis has long been an integral part of human life. 1 Synthesis is everywhere; everything that can be heard, seen, smelled, tasted, and touched involves synthesis. However, completing the chemical synthesis process faster, safer, more economically and more efficiently is still an issue of concern all over the world. Nonetheless, traditional research methods may not be sufficiently efficient. 2,3 Thus, people propose using AI techniques to assist with chemical synthesis.
AI is based on computational machines, and the theory and application of computational machines have a long history and have been applied gradually in many elds since the last century. In 1948, Claude Shannon reported that information can be encoded by binary systems, which launched the eld of information theory and set the stage for the merger of data science and chemical synthesis. 4 With years of developing electronic techniques, more AI algorithms have been developed; therefore, the applications are not restricted to the development of simple tools. [5][6][7][8][9] As shown in Fig. 1, from 2000 to 2021, studies combining AI and chemical synthesis were increasingly carried out. Especially in this ve-year period, regardless of publications or citations, they are increasing exponentially. Currently, AI applications can be realized by different methods (e.g., support vector machines (SVMs), 10 random forests (RFs), 11 articial neural networks (ANNs), 12 convolutional neural networks (CNNs), 13 recurrent neural networks (RNNs), 14 and graph neural networks (GNNs) 15 ). As shown in Table 1, AI algorithms are causing a rapid technological revolution in many elds of science, especially in the chemical eld, with high process speed and high accuracy. [16][17][18][19][20] It is not a new concept that AI is used as a chemical assisttool. Corey et al. employed AI techniques to generate chemical pathways for complex molecules in 1969. 21 AI as an assist-tool is also a promising eld of research with the potential to have a tremendous impact on drug discovery, industrial chemistry, and materials science. [22][23][24][25] For example, Li et al. used a unied and fully automated process to complete the synthesis of 14 different small molecules. 26 Mijalis et al. reported an automated method for synthesizing polypeptides. The amide bond formation time was only seven seconds, and the total synthesis of each amino acid residue only required 40 seconds. 27 Coley et al. combined AI and a series of hardware, making synthesis automation take a leap forward. They predicted and automated the synthesis of 15 drugs or drug-like molecules in 96 hours with an AI-based model. 28 Clayton et al. reported a multistep reaction automatic optimization platform that could provide substantial savings in time and resources. This AI-based method had the ability to simultaneously optimize multistep processes for multiple goals. 29 Gómez-Bombarelli et al. used a computational method to guide all stages of a materials synthesis workow. They successfully synthesized efficient molecular organic light-emitting diodes. 30 Minato et al. utilized a robotic workow modular wheel platform (MWP) to generate new heteromultinuclear metal clusters with high energy barriers for magnetization reversal. With the assistance of the MWP, the time cycle of discovering new clusters is shortened. 31 Chemical synthesis combined with AI could produce revolutionary achievements. Moreover, AI has also been widely applied in the chemical synthesis eld due to the following advantages: (1) increasing the efficiency of chemical synthesis; (2) saving time and manpower; (3) avoiding errors from manual operation; and (4) increasing personal security. Here, the concept of "AI-controlled synthesis" is proposed. An AIcontrolled synthesis system is dependent on multiple AI-aided synthesis platforms; that is, the technique of AI-controlled synthesis is a combination of several techniques of AI-aided synthesis. Therefore, a mature AI-controlled synthesis system should be made from at least two parts ( Fig. 2): soware as a controller in the control room and hardware as an executor in the laboratory. However, AI-aided synthesis systems are only applicable in some specic laboratories that have developed their own AI-aided synthesis systems. [32][33][34] Many general laboratories still use manual operations to complete synthesis. Thus, we are facing an issue. How long do we need to go to extend the applications of AI-controlled synthesis into general laboratories?
In this work, we overviewed the current workow of AI-aided synthesis and summarized current applications. We found the key restrictive processes for AI-controlled synthesis in general laboratories, and we also proposed some perspectives to overcome these limiting factors. We believe that this work could help overcome the difficulties that hinder AI-controlled synthesis in general laboratories.
2 Hardware: executor of AIcontrolled synthesis The hardware of the AI-controlled synthesis system is the equivalent of chemists' hands during manual operation. It plays the role of an executor and liberates chemists' hands. Its conguration can be as simple as a machine arm or be quite complex as a large automation platform.

The conguration of the hardware
Traditionally, chemical syntheses are completed by manual operation. Nothing is more common than this. However, people, as traditional executors, have some disadvantages: (1) they incur large amounts of time and manpower costs; (2) they make errors; and (3) they pose security risks. Therefore, the traditional chemical synthesis laboratory is encouraged to do something to promote change. AI-controlled synthesis can be much safer and much faster and could greatly reduce human errors and guarantee the repeatability of experiments. In our opinion, the conguration of automated systems as hardware should have ve modules (Fig. 3): reagent storage modules, reactors, the reaction analytic module, the purication module, and the device customization module.
2.1.1 Reagent storage modules: storage and sampling area of reagents. As the storage and sampling device of the hardware, reagent storage modules generally have four uses (Fig. 3). First, reagent storage modules store the reaction starting materials and cooperate with the injection system to complete the injection operation according to the parameters. Second, reagent storage modules can temporarily store intermediate products. Third, reagent storage modules ultimately samples the reaction products from the purication module. Last, reagent storage modules can store waste. Therefore, the reagent storage modules can be made from laboratory bottles, gas/liquid tanks, injectors, and even waste tanks ( Table 2). On the other hand, for some reactions that need special storage conditions (e.g., anhydrous and oxygen-free), reagent storage modules must be loaded with some attachments such as condition generator. Certainly, as shown in Fig. 3, chemists can also achieve specic reagent storage by using the device customization module.
2.1.2 Reactor(s): place of reaction and connector of multistep reactions. The reactor is the heart of the hardware and comprises the main structure of an AI-controlled synthesis executing system. Therefore, the reactor should have two Fig. 3 The configuration of the hardware includes five modules: reagent storage modules, reactors, the reaction analytic module, the purification module, and the device customization module.
functions: (1) as a place of a reaction executing chemical reactions and (2) as a connector connecting multistep reactions.
For the rst function, the reactor, as the area of reaction, conducts synthesis reactions. At this time, the reactor is similar to a reagent storage module and provides a place where the starting materials react, and the reaction environment needs to be adjusted according to the requirements of the reactions. However, there are some points that differ between the reactor as a place of reaction and reagent storage. The reactor as a place of reaction can be deemed a kind of reagent storage module, but it always needs some attachments (e.g., a heater, dehumidier, deaerator, and reux unit) to achieve different reaction conditions.
For the second function, the reactor is used as a connector connecting multistep reactions. When a synthesis reaction is completed in the reactor, the products should be moved into the next area (it can be the nal reagent storage module or the next reactor). For example, Angelone et al. employed the same reactors and reused them multiple times to perform multistep synthesis reactions. 35 At this time, the reactor acts as a connector. Therefore, the connector can also be deemed an important part of the reactor.
2.1.3 Reaction analytic module: tracker of the reaction process. Regardless of manual operation in general laboratories or AI-controlled operation in automation laboratories, chemists always care about a key question: how is my reaction going? Therefore, it is necessary to use a reaction analysis module in the AI-controlled synthesis system. The reaction analysis module allows for the real-time monitoring of chemical process transitions by applying ow nuclear magnetic resonance (NMR), 28 Fourier transform infrared (FT-IR) spectroscopy, 36 UVvisible (UV-vis) spectrophotometry, 37 electrospray mass spectrometry (ES-MS), 38 liquid chromatograph-mass spectrometry (LC-MS), 39 and high-performance liquid chromatography (HPLC) 40 techniques. There are already advances being made in the development and application of reaction analytics modules. Cronin et al. reported real-time online NMR to create a selfoptimizing reactor system for data analysis. 41,42 Among them, LC-MS, NMR and HPLC are more compatible with automated synthesis platforms because of their good selectivity, high separation efficiency, wide application range, and high detection sensitivity.
2.1.4 Purication module: purication and collection area of the main product. When a synthesis reaction or even a step of the synthesis reaction is completed, chemists need to purify the product(s) with which they want to obtain the nal main product or perform the next reaction. Therefore, AI-controlled synthesis systems need to have one or several purication module(s). For example, Grob et al. used the stability of Nmethyl iminodiacetic acid (MIDA) boronated under different reaction conditions, and different reagents and byproducts could be puried by the same process. 43 The purication module generally uses chromatographic techniques to isolate or purify products. This means that the purication module usually works with the reaction analytics module. However, the purication module technique is not mature, and the purication process sometimes requires manual operations. 44 (2) Application promotion remains at a low stage, almost just for customization of specic reactors 2.1.5 Device customization module: exible designer of the hardware. Generally, normal devices are enough to support most chemical synthesis reactions. However, chemists still need to build some specic devices for carrying out some reactions under specic conditions. For example, Bubliauskas et al. used a 3D printing technique to build different specic reactors for 3 different chemical synthesis reactions and 11 chemical processes. This work has decreased negative effects from reactors, thereby avoiding frequent modication of chemical codes of the soware for different reactions. 45 Tailoring specic reactors for different chemical reactions provides a new way of choosing suitable reactors for the hardware. For a mature AIcontrolled system, the device customization module should be stronger. In addition to customizing specic reactors, the device customization module can also design and build specic components for reagent storage modules and the purication module.

Applications and aws of current hardware
The hardware of the AI-controlled synthesis system should be made from the ve parts discussed above. There are some applications that have been published. Perera et al. reported an automated ow-based synthesis platform for nanomole-scale reaction screening and micromole-scale synthesis. 33 Taking into account the potential susceptibility of the assessed chemical to air and moisture, the reactor was congured in a glovebox (<20 ppm O 2 , <20 ppm H 2 O). A convenient and efficient purication module could greatly reduce manual work. Rougeot et al. described a device that enabled automatic sampling of soluble components (solutions only) and slurries (solids and solutions) in parallel. 46 Liu et al. developed an automatic platform "SPS-ow" for processing solid-phase synthesis. By employing the attachment high-pressure gas pump, SPS-ow completed the solid-phase synthesis reaction in six steps in 32 h and obtained a good yield (65%). This work opened a door for the subsequent development of AI-controlled solid-phase synthesis. 47 However, existing systems are not perfect. There are some problems with the hardware (Table 2). Table 2 shows that the main aw is the blockage of pipes or pumps with slurry. Flow system clogging includes several types: (1) crystallization of dissolved salts; (2) oil phase of the multiliquid phase; (3) particle deposits/bubbles present in the uid; (4) chemical reaction (such as polymerization or decomposition) with wall materials or reactants, leading to solid precipitation. Therefore, precipitates formed during the reaction lead to irreversible blockage of reactor microchannels. This aw has become a main barrier to the applications of AI-aided synthesis in general laboratories. Other aws are mainly from devices with imperfect technologies. For example, some general reaction devices are not suited to specic reactions that need customized devices, the real-time tracking technique of the reaction analytics module needs to be improved, and manual operations of the purication module need to be resolved. Regardless of the aw, they have reduced the performance of the hardware. Future hardware should deal better with different phases (especially solid), and require the seamless integration of sampling, reaction operations, purication separations, and analysis procedures.

Software: controller of AIcontrolled synthesis
The soware is the most important part of the AI-controlled synthesis system. As the center, the soware not only stores and processes various data but also controls the behaviors of the hardware. This means that the AI-controlled synthesis system will be a lifeless system if there is no soware. Therefore, the soware should have the functions of recording, storing, processing and invoking data. Fig. 4 The configuration of the software includes four parts: the reaction information module, reaction prediction module, reaction operation information module, and feedback recording module.

The conguration of the soware
The soware is the core/brain of the system and the prerequisite for automation to become a reality. Generally, the soware in automated systems for synthesis has three main functional categories: (1) controlling the hardware operating the synthesis reaction, (2) monitoring/analyzing the synthesis process, and (3) designing the synthesis strategy. To date, chemical synthesis, especially organic synthesis, is still a labor-intensive experimental subject. Robotic systems for the chemical domain are becoming increasingly popular due to the lack of general chemical programming languages and the ability to directly access natural language documents. A series of powerful AI algorithms are emerging quickly. They will perhaps make chemical synthesis no longer dependent on excessive human intervention. The rapid development of AI technology has brought rapid progress in experimental automation. The soware should be made from four soware modules (Fig. 4): (1) the reaction information module; (2) the reaction prediction module; (3) the reaction operation information module; and (4) the feedback recording module.
3.1.1 Reaction information module: database of molecules and reactions. The reaction information module is the chemical reaction information exchange hub of the soware. It obtains, stores, and invokes the data information of different molecules and reactions. There are many online molecule and reaction databases (e.g., SciFinder, Reaxys, Chemical reactions from US patents (1976-Sep 2016) and so on). Therefore, the reaction information module simply needs to connect to the internet and download data from online databases. [48][49][50][51][52] Sometimes, chemists can also build personal reaction datasets and upload them to the reaction information module. For example, Coley et al. used deep learning with a Transformer Encoder module to get reaction and operation information directly from the published papers, 53 and then developed the open-access program Open Reaction Database (ORD) as a general database for getting reaction information, so that chemists can get chemical information more easily. 54 These studies developed a new way by which the soware can also get reaction information from literature studies, not only from databases.
Moreover, the reaction information module should have a backup device as an attachment. When a network failure happens, the backup device can upload data to the reaction information module. If the main body of the reaction information module does not work, the backup device can also get data directly from online databases.
3.1.2 Reaction prediction module: providing the optimal synthesis pathway. This module is the core of the soware. The reaction prediction module, as a storing and invoking module, collects information on different reaction mechanisms. Simultaneously, the reaction prediction module must be used as a data processing module to obtain the export data from the reaction information module and to nd an optimal synthesis pathway for the hardware. This means that the reaction prediction module needs to integrate an AI program to predict the synthesis pathway. The reaction prediction module mainly has two types: (1) retrosynthetic analysis and (2) forward-synthetic analysis.
Retrosynthetic analysis is more frequently used by current programs 46 because chemists oen know products more than reactants. Many retrosynthetic analysis programs based on AI are available. For example, Automated System for Knowledgebased Continuous Organic Synthesis (ASKCOS) is a frequently used AI retrosynthesis prediction program. 55 Other programs such as IBM RXN, 56 Chemical.AI, 57 and Molecule.one 58 are famous programs. IBM RXN is also the rst free AI web service for predicting chemical reactions. Forward-synthetic analysis is actually a reaction mechanism inference and is commonly used in ab initio or quantum-based prediction. 59,60 AI-based forwardsynthetic prediction is less common than retrosynthetic analysis. Although forward-synthetic analysis programs are few, it does not mean that there is no program to perform forwardsynthetic analysis in the AI eld. ASKCOS can also perform forward-synthetic analysis. New reaction prediction programs have also been developed recently. For example, researchers from AstraZeneca developed two AI-based open-source prediction programs REINVENT 61,62 and GraphINVENT 63-65 for guiding new drug molecule synthesis and exploring new chemical places of new drug molecules.
3.1.3 Reaction operation information module: processing hub of operation data. The data of the reaction operations are the same as those of the manual experimental workow. These include two aspects: (1) the conditions and environments of a chemical synthesis reaction (e.g., temperature, pressure, pH, catalyst, and solvent) and (2) the operations of a chemical synthesis reaction (e.g., dosage, adding order of reactions, and order of operations). Therefore, the operation information module can obtain these data in three ways: (1) directly from online databases, (2) from the reaction information module, and (3) from the feedback information module to obtain the real-time feedback of the hardware (Fig. 4). Fig. 4 also shows that the export data from the operation information module mix with the export data from the reaction prediction module and become a command to control the hardware. Therefore, the reaction operation information module is the toolbox of the soware.
3.1.4 Feedback recording module: processing real-time data from the hardware. When the hardware is working, it can generate log les as feedback data to send back to the soware. These feedback data are similar to the experimental data in general laboratories, which are very important and need to be recorded and processed. Hence, the soware needs the feedback information module to record the feedback data from the hardware. As shown in Fig. 4, the feedback recoding module has three main functions: (1) obtaining the real-time feedback data from the hardware; (2) uploading the real-time operation data to the reaction operation information module; and (3) invoking the real-time experimental data into the personal dataset to improve the reaction information module.
The feedback recording module rst acts as a scout base for the soware. It can be based on real-time feedback data from the hardware and help the soware modify old commands or send new commands to the hardware. Therefore, the soware can also use the feedback data from the hardware to improve its performance. Therefore, the feedback recording module plays a key role in supervising machine learning and improving soware strategies.

Applications and aws of current soware
Recently, a series of powerful functions of AI algorithms have been rapidly developed in parallel, and they could greatly facilitate the development of platforms that could automate custom general synthesis routes. There are many applications for the soware to be reported. For example, Burger et al. developed an AI robot named the mobile robotic chemist and proposed the concept "AI Chemist". AI Chemist can complete research on approximately 1000 precatalyst compounds a week, which is a burdensome task that a PhD student needs 4 years to complete. 66 Cronin et al. developed a general system for chemical digitization and automatic synthesis that could automatically read the literature. 67 This is another epoch-making achievement in the eld of AI-based chemistry following the Chemputer. 68 By combining Chemical Description Language (cDL) and the easy-to-use interface ChemIDE, ChemIDE allows users to program chemical synthesis without any coding knowledge. This "paper in, product out" AI chemist took synthesis automation to a new level. Surprisingly, Stuyver et al.
reported this year that they combined QM with GNN learning to develop an ml-QM-GNN module for forward-synthetic analysis. The ml-QM-GNN module can not only predict the activation energy, reactivity, and chemoselectivity of reactants but also nd the preferred reaction mode. 69 This work provided a new way of developing forward-synthetic analysis methods with a combination of ab initio and machine learning. Applications of soware are becoming increasingly widespread. Soware is the brain of automated platforms that help chemists control the entire synthesis process. Existing soware has much room for improvement in designing reaction routes.
There are still some aws in the soware that need to be considered. In Table 3, we nd that the current soware as a controller of AI-controlled synthesis systems has two main aws: (1) AI-based forward-synthetic analysis programs are few; (2) weakly integrating computer-aided synthesis planning (CASP) tools; and (3) the data of failed syntheses are not attended to. Many chemists want to know the synthesis route for a product. Retrosynthetic analysis has been developed and used widely. However, many laboratories focus on developing new synthesis methods and generating new compounds. Traditional forward-synthetic analysis is mainly based on QM calculations but not AI calculations. This hinders AI-controlled synthesis systems in laboratories that are interested in nding new synthesis methods and new compounds. Because pure retrosynthetic analysis can obtain many synthesis pathways with many different reactions, many of the predicted synthesis pathways are unhelpful for some general chemosynthesis laboratories. Therefore, a mature forward-synthetic analysis with special reactants can guide these general chemosynthesis laboratories to generate new compounds or to explore new synthesis methods. This is more helpful than retrosynthetic analysis to increase the success rate of synthesis experiments in wet laboratories. Some soware weakly integrates CASP tools. This makes the soware rely too much on the internet (e.g., online CASP tools and online databases). Moreover, the data of failed syntheses as feedback can make the soware avoid previous mistakes. Thus, soware with more kinds of synthetic analysis, including CASP tools, should receive more attention in the future.

Automation control component: the bridge between software and hardware
We have pointed out that the soware controls every behavior of the hardware to perform AI-controlled synthesis. Chemists need a/some device(s) to connect the soware and the hardware  (2) Sending data to information modules (Fig. 2). For a control system, the automation control component (also called the digitization control component) is usually the rst choice. There are some advantages to the automation control component: (1) mature technology; (2) various implementation methods; and (3) running stably. The automation control component is generally embedded. This means that the automation control component does not need to occupy a larger space. Currently, people can directly use microcontroller units or employ integrated chips or even integrated programs into the soware.
As shown in Fig. 5, we can directly know the working principle of the automation control component. First, we introduced that the soware controls the hardware to execute synthesis reactions in the AI-controlled synthesis system. As shown in Fig. 5, the soware sends commands that include reaction and operation information and synthetic prediction information to the automation control component. Then, the automation control component processes them and hands them to the hardware to perform sampling, reaction, purication, etc. Second, we note that the soware should have a feedback information module to obtain feedback information from the hardware. Actually, the automation control component also plays the role of a sensor. The different original feedback signals mainly come from the hardware and are input to different sensor units of the automation control component. Then, the automation control component processes these feedback signals and sends them to the feedback information module of the soware so that the soware can more exibly correct the controlling strategies. Overall, the automation control component is a connected hub that connects the soware and the hardware.
Aer introducing the working principle of the automation control component, we obtain a question. Which type of automation control component has been used for connecting the soware and the hardware? Hammer et al. reported that chemists mainly use microcontroller units with Python or C++ codes to control the hardware for simple synthesis. 70 Prabhu et al. pointed out that chemical researchers can choose multiple types of microcontroller units to make the soware control the hardware (Fig. 5). Chemists can directly use microcontroller boards (e.g., Arduino 101, Ardbox PLC, chipKIT Uno32, and Particle Argon) or choose single-board computers (e.g., Raspberry Pi 3 and BeagleBone Black). 71 Wilbraham et al. reported that they utilized cDL to drive the microcontroller unit "chemical processing unit (ChemPU)" for controlling, optimizing, and exploring synthesis reactions. 72 In this year, Cronin et al. improved the compatibility between cDL and ChemPU. They employed this more mature automatic platform to process 53 kinds of chemical reactions, which are among the 103 kinds of reactions that are encoded by cDL, and obtained good results and high yields. This work makes it possible to replicate chemical synthesis experiments from previous literature, and also greatly improves the performance of current AI-controlled synthesis systems. 73 The real jobs of chemists are just to rewrite or to modify codes in C++, Python, or even in their own machine languages.
According to these reports above, we nd that an automation control system can already be executed on a personal computer. But there are still two unfavorable factors around the automation control system in current AI-aided synthesis platforms: (1) the technical barrier is high for general chemists; (2) opensource application programming interfaces (APIs) of commercial programs on third-party platforms are lacking. Although applications of the automation control system may have been sufficient for the automation control component of AI-aided synthesis, the development of a more intelligentized automation control component of AI-aided synthesis should become chemists' focus in the future.

Review
Chemical Science From the contents above, we can nd that AI-aided synthesis systems are increasingly widely used in the chemical synthesis eld. However, the rate-determining step of AI-controlled synthesis systems is rarely mentioned, which makes it difficult to apply in general laboratories. Recently, many chemists have been developing new devices for AI-controlled synthesis and trying to improve the performance of AI-controlled synthesis systems. Therefore, we have some perspectives for the subsequent development of AI-controlled synthesis systems. We propose some viewpoints for the hardware of AIcontrolled synthesis systems: (1) More adaptable. The hardware should execute the synthesis reaction in different phases. It needs to add a new main module as a cleaner to clean blockage from solid or multiliquid reagents. The module can be an ultrasonic generator or a high-pressure air/liquid pump.
(2) More customization devices. General devices are sometimes not suited to some specic synthesis reactions. This is a hindrance to AI-controlled synthesis in general laboratories. In addition to 3D printing techniques, more methods of updating or even building specic devices should be developed.
(3) More automatic. Manual operations of the purication module reduce the automatic performance of the hardware. The purication module should load ow controllers and automatic replacement devices to simulate and replace chemists' hands.
(4) Quicker. Real-time tracking is important for the process of a reaction. The reaction analytic module should be quicker to analyze the situation of products.
(5) More convenient. An automated chemical operation platform should save the time cost of chemical experts. The system should not be too complicated such that it would require much training for operators to operate the equipment.
(6) Lower economic investment. Fund-rich laboratories are not everywhere. Thus, lower cost is helpful to make AIcontrolled synthesis migrate into general laboratories.
We propose some viewpoints for the soware of AIcontrolled synthesis systems: (1) AI-based forward-synthetic analysis. The development of forward-synthetic analysis programs should be attended to. Many general laboratories focus on exploring new synthesis methods and new products from specic reactants. Predicting possible products from known reactants is necessary. On the other hand, the success rate of choosing the right synthesis reaction path is higher with forward-synthetic analysis. Hence, they need forward-synthetic analysis rather than retrosynthetic analysis. Forward-synthetic analysis has some advantages that retrosynthesis analysis does not have. It is helpful to extend AIcontrolled synthesis systems into laboratories that focus on developing new synthesis methods or discovering new compounds.
(2) Paying attention to negative data. Chemists generally pay attention to the positive data that they obtain, but the negative data (e.g., infeasible reaction data and low yield reaction data) are always skipped. Therefore, the reaction information module of the soware should add the information on negative data. Negative data can offer failed examples for the AI-controlled synthesis system so that the AI-controlled synthesis system can avoid unnecessary mistakes.
(3) Combined with CASP tools. At present, chemical automation studies focusing on synthesis planning are rarely reported, and they are much more complicated than the automation of the synthesis process. Thus, an automated system that includes CASP tools should receive more attention in the future.
(4) Greater interactivity. The soware should be able to intervene in the entire reaction process in a timely manner while monitoring the reaction process to ensure the synthesis effect and avoid potential safety hazards.
(5) User-friendliness of the CASP programs integrated into automated systems. The existing command line programs may be suitable for chemo-informatics, but some chemists use graphical user interfaces (GUIs).

Conclusion
A series of powerful advancements in the hardware and soware of AI-controlled synthesis systems are emerging quickly and in parallel. They have the potential to make synthesizer development possible and automate custom and general synthesis routes. AI-controlled synthesis has the following advantages: (1) safety; (2) high speed; (3) reproducibility; and (4) low cost of synthesizing compounds. We have pointed out some limiting factors that mean AI-controlled synthesis systems cannot be extended to general laboratories. The hardware of the AI-controlled synthesis system should include a cleaner module to keep the hardware channels open. Regardless of solid or multiliquid reagents, the improved hardware can use the cleaning module to clean blockages in channels. For the soware of the AI-controlled synthesis system, its reaction prediction module should have more mature AI-based forwardsynthetic analysis functions. It is helpful to extend the AIcontrolled synthesis system into general laboratories that are interested in developing new organic synthesis methods. In summary, an AI-controlled synthesis system could quickly and accurately control reaction conditions such as temperature and pressure to handle sensitive chemicals and dangerous reactions more safely. [74][75][76] Fortunately, some groups are trying to overcome these challenges from the aws of current AI-controlled synthesis systems and have made some achievements. 35,47,69,73 Beyond all doubt, with the development of AI algorithms of the soware and engineering technology of the hardware, we believe that chemists will quickly move toward the goal of AIcontrolled synthesis. Therefore, extending the method of AIcontrolled synthesis into general laboratories should not take much longer.

Conflicts of interest
There are no conicts to declare.