Exploring the Synergistic Potential of Artificial Intelligence and Machine Learning in Chemistry

Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.

Computer modeling, machine learning, and artificial intelligence are currently considered cutting-edge topics in chemistry and materials science. The application of information technologies in natural sciences can help researchers collect big data and understand patterns that are not obvious to humans. In this perspective, I would like to highlight the recent achievements of our research group and other researchers in relation to computer modeling and machine learning in chemistry and materials science.

The current study aimed to criticize the existing literature on the utilization of artificial intelligence (AI) and machine learning (ML) in teaching and learning chemistry. A comprehensive critical literature review was conducted using electronic databases such as Scopus, PubMed, ISI, Google Scholar, ERIC, Web of Science, and JSTOR. In this regard, 62 articles were extracted from these electronic databases. During the selection of the literature inclusion and exclusion criteria were applied. The inclusion criteria include empirical and theoretical studies examining the effectiveness, challenges, and opportunities of AI/ML, and articles from 2018 to 2024 and written in English. On the other side, the exclusion criteria include literature that unrelated to education, lacking empirical evidence, or not peer-reviewed, as well as non-English publications, and published before 2018. This was done to gain insights into the current implementation status of AI and ML as well as critical issues of using these approaches in chemistry education. The study employed a critical review of the literature, which involves a critical analysis of the themes and concepts that emerge from the selected literature and identifies the opportunities and challenges surrounding the utilization of these technologies. The results revealed that there are opportunities for the integration of AI and ML in chemistry education, including personalized learning experiences, teacher assistance, and accessibility to learning materials. In this regard, intelligent tutoring systems and adaptive learning platforms were identified as potential aides for teachers in various aspects of teaching. The study also revealed the limitations and challenges surrounding AI and ML, such as the dependence on preexisting data, potential biases in models, and concerns around data privacy and security. Moreover, the findings also indicated that the implementation of AI and ML in chemistry education is still in its juvenile stage. Thus, teacher training programs are needed to equip teachers with the necessary skills for the use of these technologies effectively in the classroom. In addition, more efforts should be made to facilitate research, collaboration, and the development of policies and regulations that ensure responsible use of these technologies in the teaching and learning process.

Machine learning in experimental materials chemistry

A review of research in the field of chemistry that incorporates machine learning is essential to identify recent developments and explore its potential applications. Published research articles provide an opportunity to analyze emerging research trends. The use of natural language processing (NLP) technology not only accelerates text data analysis but also enhances accuracy in understanding the content and context of scientific articles. Previously, trend analysis in ophthalmology research had been conducted using Zero-Shot Learning. In this study, an analysis of chemistry-related articles focusing on machine learning was carried out using a multi-turn prompting technique. The process began with data collection through web scraping of abstracts containing the keywords "machine learning" and "chemistry." The retrieved data was then tabulated and analyzed using a Large Language Model (LLM) with a Multi-Turn Prompting approach, where general prompts were initially used, followed by deeper exploration based on previous responses. Additionally, statistical descriptive analysis was performed using targeted prompts. Analysis of 200 article abstracts identified seven key terms related to the use of machine learning in chemistry: chemical (138 articles), protein (119 articles), drug (107 articles), structure (100 articles), molecular (96 articles), chemistry (91 articles), and quantum (84 articles). Furthermore, three dominant research topics were found in the intersection of chemistry and machine learning: protein and molecular structure, quantum chemistry, and drug discovery. The number of articles on machine learning in chemistry began to rise in 2012 and saw a significant increase in 2019. The findings suggest that there are still many opportunities for developing machine learning applications in chemistry, particularly in quantum chemistry. This field only began to gain attention in 2013, and the number of published articles remains relatively low each year, indicating that it is still in the early stages of exploration.

Theoretical and computational chemistry (TCC) is a set of theories and models that, over the years, were refined to the point that it is possible to determine measurable quantities with precision, predict experimental results, and provide fundamental insights into chemical phenomena and mechanisms that may be difficult or impossible to observe experimentally. Machine Learning (ML), on the other hand, is a subfield of Artificial Intelligence (AI) that applies different types of statistical methods to a large volume of data or a smaller volume of precise data, combined with high computational power, enabling the discovery of complex patterns and production of explanations inaccessible through human deductive reasoning and intuition alone or using traditional scientific methods. Recently, ML, combined or not with TCC methods, has emerged as a transformative force, bringing significant advances in chemistry and materials science. This review surveys basic ML concepts and their applications in chemistry, focusing on supervised and unsupervised learning approaches, data preprocessing, and model development workflows, exploring the most relevant ML algorithms selected for their specific usefulness in chemical applications. Integrating ML with traditional computational chemistry methods, such as density functional theory, is highlighted as a powerful synergy for accelerating materials discovery and design. Key areas of impact discussed include High-Throughput Virtual Screening (HTVS) of molecules and materials, spectroscopy (including UV-Vis and fluorescence), organic electronics (such as solar cells and organic light-emitting diodes), potential energy surfaces, and molecular dynamics. It also addresses critical aspects of ML in chemistry, including data representation, model interpretability through explainability techniques, and the emerging role of large language models (LLM). Tips for acquiring knowledge of ML for practical applications in chemistry are given.

The fusion of machine learning (ML) and artificial intelligence (AI) is experiencing a dramatic transition in the field of pharmaceutical research and development. This study examines the several effects of machine learning (ML) on different phases of medication discovery, development, and patient care. The capability of ML to quickly process huge chemical libraries and forecast interactions with target proteins is studied, starting with compound screening and selection. The potential for fewer false positives and negatives, improved hit prediction accuracy, and ensemble technique use are underlined. The part that machine learning plays in enhancing Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile is then explained. ML models anticipate compound actions inside the human body by analyzing molecular structures and characteristics, improving assessments of drug safety and efficacy. The article goes into further detail about predictive modeling, highlighting how machine learning may be used to find prospective therapeutic targets and confirm their applicability. The combination of multi-omics data, deep learning, and the possibility to identify similar molecular pathways across diseases highlight the game-changing potential of machine learning in this field. The article also covers the use of ML in clinical trials, highlighting its benefits for trial planning, patient recruitment, real-time monitoring, and individualized therapy predictions. By utilizing computational analysis and quantum physics, the power of machine learning-driven de novo drug creation is examined, revealing the potential to develop new therapeutic candidates. In this article, the ethical issues surrounding AI-driven drug discovery are discussed, with a focus on the necessity of transparent data utilization, human oversight, and responsible data consumption. The report ends by predicting ML's potential for pharmaceutical R&D in the future. Accelerated drug discovery pipelines, the rise of customized medicine powered by predictive models, optimized clinical trials, and a change in medication repurposing tactics are all envisaged in this. The report emphasizes the revolutionary potential of ML in altering pharmaceutical research and development while noting obstacles in data quality, model interpretability, ethics, and interdisciplinary collaboration. It is suggested that the ethical integration of AI technologies, interdisciplinary cooperation, and regulatory modifications are essential steps to unlock the full potential of ML and AI and, ultimately, provide patients throughout the world with safer, more efficient, and individualized treatments.

Computational chemistry, at the intersection of theoretical chemistry and computer science, employs various models to analyze molecular structures and properties, enabling the understanding and prediction of intricate chemical processes. The integration of artificial intelligence (AI) has revolutionized several fields, particularly in materials chemistry, with applications spanning drug discovery, materials design, and quantum mechanics. However, challenges related to quantum system complexity, model interpretability, and data quality remain a few of the Achilles’ heel of AI applications. This paper provides an overview of AI’s evolution in computational and materials chemistry, focusing on several applications. AI’s transformative potential in materials chemistry is emphasized, facilitating precise material property predictions, crucial for industries reliant on materials innovation. In materials chemistry, AI has led to substantial advancements, enabling the rapid discovery of materials with tailored properties. Yet, the challenges of modeling complex quantum systems, achieving model interpretability, and accessing high-quality data remain. The integration of AI into computational and materials chemistry promises to reshape the field, revolutionizing chemical research, materials design, and technological innovation. In order to harness AI’s full potential, transparent AI models, advanced quantum simulations, optimized data utilization, scalable computing, interdisciplinary collaboration, and ethical AI practices are essential.

Computational Medicinal Chemistry

Applications of machine learning in chemistry are many and varied, from prediction of structure–property relationships, to modeling of potential energy surfaces for large scale atomistic simulations. We describe a generalized approach for the application of machine learning to the classification of spectra which can be used as the basis for a wide variety of undergraduate projects. While our examples use FTIR and mass spectra, the approach could equally well be used with UV–visible, Raman, NMR, or indeed any other type of spectra. We summarize a number of different unsupervised and supervised machine learning algorithms that can be used to classify spectra into groups, and illustrate their application using data from three different projects carried out by fourth year chemistry undergraduates. The three projects investigated the ability of the various machine learning approaches to correctly classify spectra of a variety of fruits, whiskies, and teas, respectively. In all cases the algorithms were able to differentiate between the various samples used in each study, and the trained machine learning models could then be used to classify unknown samples with a high degree of accuracy (>98% in many cases). Depending on the extent to which students are expected to write their own code to perform the data analysis, the general model adopted in this work can be adapted for a variety of purposes, from short (one to two day) practical exercises and workshops, to much longer independent student projects.

The 2022 SLAS technology ten: Translating life sciences innovation.

Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry

The success of machine learning in chemistry is fundamentally underpinned by the information fidelity of molecular representations. Despite their widespread adoption for efficiency and interpretability, Morgan fingerprints harbor a long-overlooked and fundamental flaw: bit collisions. This phenomenon erroneously maps distinct chemical substructures to identical positions, systematically corrupting structure-property relationships and severely compromising model interpretability. To address this challenge, we introduce Collision-Free Morgan Fingerprints (CF-MF), a principled framework that guarantees the integrity of substructure information through an adaptive, data-driven sizing mechanism. Through a comprehensive evaluation across 25 diverse datasets (> 50,000 molecules) and multiple machine learning paradigms, we demonstrate that CF-MF delivers consistent and significant performance gains up to 16.81% RMSE reduction in regression and 11.1% accuracy increase in classification. More critically, by eliminating attribution errors caused by collisions, CF-MF fundamentally restores chemical interpretability and expands the reliable prediction domains of models by 60-100%. Our information-theoretic analysis reveals a strong correlation between collision-induced entropy loss and performance degradation (R2 = 0.854, p < 0.001), establishing information fidelity as a fundamental design principle for next-generation molecular representations. It also achieves performance competitive with state-of-the-art deep learning models while retaining the simplicity and intuitiveness of traditional fingerprints. This work provides a more reliable and trustworthy foundation for AI-driven drug discovery, materials science, and environmental assessment.Scientific contributionWhile bit collisions in Morgan fingerprints have been acknowledged for decades, this study is the first to systematically quantify their impact on machine learning performance and provide a principled, reproducible solution applicable to any molecular dataset. We establish a novel information-theoretic framework that directly links collision-induced entropy loss to predictive degradation, offering the field a quantitative criterion for evaluating molecular representation fidelity. Beyond performance gains, our work uniquely demonstrates that eliminating collisions restores chemically valid SHAP attributions-addressing a critical but previously unrecognized barrier to trustworthy AI interpretation in chemistry.

Machine learning is a logical extension of automation using computer systems. While a large number of different areas of human activity have been improved by algorithmic software, a large number of other problems remain unsolved because creating an algorithm for them is almost impossible. One of these fields is science. The empirical approach is still main approach in achieving results, because for many studies there is still no clear mathematical apparatus. Machine learning is the solution that allows to save resources and speed up the research process. Conducting experiments always leads to collecting data about the results. Machine learning algorithms allow to use this information to build a model capable of predicting the results of experiments or the properties of new compounds. Within the scope of this article, the effectiveness of different algorithms, both standard and ensemble algorithms, is tested on the data obtained from experiments with solute diffusion. As a result, the effectiveness data of various algorithms were calculated using the formulas of root mean square error, as well as mean absolute percentage error. An example and description of the process of building different types of machine learning models are given.

This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a two-stage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction curation, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applications in organic synthesis.Scientific contributionThe proposed automated preprocessing tool for chemical reaction data aims to identify errors within chemical databases. Specifically, if the errors involve atom mapping or the absence of reactant types, corrections can be systematically applied using reaction templates, ultimately elevating the overall quality of the database.Graphical