Theoretical Chemical Design with Machine Learning: Model Development and Applications

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Chemists identify the right substance for a given task (e.g., a drug molecule that binds to a target protein) by studying the structure and properties of molecules and materials, as well as the changes they undergo. This is challenging because chemical compound space is vast (i.e., there are innumerable potential chemical substances) and experimental measurement of chemical properties is time-consuming, resource intensive, and sometimes even unethical. Traditional theoretical chemistry develops physical models and uses computational resources to calculate chemical properties. Conventional methods based on accurate quantum mechanics calculations are often inapplicable because they are too expensive (i.e. their computational cost grows exponentially with the size of the molecule), while conventional methods based on fast molecular mechanics simulations are often too inaccurate to reliably predict new phenomena. These technical and practical limitations motivate my interest in computer-aided molecular design and, specifically, in developing 1) new mathematical models based on state-of-the-art machine learning (ML) algorithms and 2) new tools to qualitatively and quantitatively predict chemical phenomena and ultimately design molecules with desirable properties. Just as human chemists learn from past experiences to make predictions about the properties of new molecules, in ML a mathematical model is trained to leverage the results of previous experimental measurements or computational studies to predict the properties of new molecules. The proposed models are applicable to many problems in chemistry and aim to achieve the accuracy of reliable quantum chemistry methods at the cost of molecular mechanics. Thus, they can be used for the systematic, rapid, and robust screening of large molecular databases, thereby guiding subsequent experimental and theoretical studies. It is important to note that ML methods are applicable even where experimental measurements (e.g., chemistry in extreme environments, astrochemistry) and computational simulations (e.g., physiological responses like toxicity and carcinogenicity) are impossible. We disseminate our models through software packages including ChemTools, a free and open-source platform for discovering and exploring chemical concepts, which so far has attracted users from ~10 international research groups. There is also an educational impact to my research. For example, we have organized 3 ChemTools workshops so far (Chile 2017, China 2018 & France 2019) educating students and postdocs on Python and conceptual quantum chemistry. ChemTools is used in teaching (under)graduate courses to facilitate grasping theoretical concepts and to familiarize students with programming, which is among the most marketable technical skills. More importantly, through my research, I will train a diverse group of 5 Ph.D., 4 M.Sc. and 5 undergraduate researchers, thereby empowering the next generation of leaders.