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Artificial Intelligence in Drug Discovery: Into the Great Wide Open
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Journal of Medicinal Chemistry

Cite this: J. Med. Chem. 2020, 63, 16, 8651–8652
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https://doi.org/10.1021/acs.jmedchem.0c01077
Published July 8, 2020

Copyright © 2020 American Chemical Society. This publication is available under these Terms of Use.

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SPECIAL ISSUE

This article is part of the Artificial Intelligence in Drug Discovery special issue.

We are pleased to introduce the Special Issue “Artificial Intelligence in Drug Discovery” highlighting the emerging role of artificial intelligence (AI) in pharmaceutical research. A focal point of this issue is illuminating how AI approaches are beginning to impact the practice of drug discovery. The Special Issue contains articles and perspectives that view AI in drug discovery from different angles.

First, we thank our authors for their high-quality and thematically diverse contributions. In addition, we thank many reviewers of submissions to this Special Issue who often have carefully evaluated manuscripts at short notice. Finally, we gratefully acknowledge the editorial office staff for their continuous support.

Of note, a significant number of the many submissions to this Special Issue could not be further considered because they did not meet the Author Guidelines and general acceptance criteria for publication of computational studies in the Journal of Medicinal Chemistry.

The papers in this issue cover a variety of method development efforts and practical applications that provide us with a flavor of how AI is entering the drug discovery arena. Of course, machine learning methods have already been applied for more than two decades in cheminformatics and computational medicinal chemistry, but deep learning has more recently become a hot topic in many areas of science including chemistry. The Special Issue pays tribute to these developments. In addition to providing a number of applications of AI in drug discovery, the Special Issue contains papers that address several key features in the field, as highlighted below.

Molecular Representation. A machine learning method creates a model that is used to establish relationships between input data and an observable end point. In medicinal chemistry, we typically model the relationship between chemical structure and physical properties or biological activity. A key component in this process is the representation that is used to map a molecular structure into a form that can be processed by a machine learning algorithm. Until recently, molecules were often represented as vectors encoding the presence or absence of substructures of structural patterns in a molecule. In recent years, a number of groups have developed methods that use deep learning to create new “learned representations”. While the predictive power of these learned representations is still an open question, they have shown promising initial results. One of the perspectives in this issue provides an excellent introduction to the motivations behind and applications of learned representations (Chuang et al.; DOI: 10.1021/acs.jmedchem.0c00385). In addition, this issue features two articles on the topic of learned representations. One paper describes the application of a technique known as graph attention networks, which enable a neural network to focus on the most important features (Xiong et al.; DOI: 10.1021/acs.jmedchem.9b00959). The other paper reports the benchmarking of a novel molecular representation on a large set of pharmaceutical ADME data (Feinberg et al.; DOI: 10.1021/acs.jmedchem.9b02187).

Model Interpretation. One drawback to many machine learning approaches is that they mostly are “black box predictors”. Accordingly, in a drug discovery setting, one inputs a chemical structure and receives a result without any explanation of how or why the prediction was generated. Ideally, we would like to have machine learning models that could be interpreted by human users. The explanations produced by these models would serve two purposes. First, a user could examine the explanation, confirm that it agrees with theoretical and experimental foundations, and establish some degree of confidence in the prediction. Second, the explanation of the model could provide clues into the mechanistic drivers behind the biological activity being modeled and provide inspiration for the design of new molecules. The Special Issue contains a paper describing new methods that enable machine learning models to provide interpretable results for chemical data sets, regardless of the algorithms that are used (Rodríguez-Pérez et al.; DOI: 10.1021/acs.jmedchem.9b01101).

Recommendation Systems. Computer systems that provide recommendations have become part of our everyday lives. For example, e-commerce sites provide recommendations based on our purchasing history. Online streaming sites recommend music and videos that we may enjoy. A paper in this issue extends this concept to the medicinal chemistry laboratory. The authors describe how the concept of “people who bought this also bought this” can be extended to recommending routes for organic synthesis, three-dimensional structures of similar compounds, and assays that may provide additional insights (Rohall et al.; DOI: 10.1021/acs.jmedchem.9b02130).

Reaction Design. One of the areas in chemistry where AI is currently making headway is predicting and modeling new chemical reactions and synthetic routes. A perspective in this issue highlights recent developments in this emerging area of research and provides an outlook (Struble et al.; DOI: 10.1021/acs.jmedchem.9b02120).

Generative Models. Despite more than 30 years of advances in computational chemistry, many, if not most, of the ideas for new molecules in a drug discovery program originate from the imagination and ingenuity of a medicinal chemist. Beginning in the 1990s, a number of groups produced computer programs for performing de novo molecular design. These programs often (but not always) operated by “growing” an existing molecule in the context of a protein binding site. However, while there were some stories of success from de novo design, the technique failed to achieve mainstream adoption. Over the past few years, we have seen the rise of a related technique known as generative modeling. This field, which has its origins in language models and image generation, takes as input a set of molecular structures, which are encoded as a continuous low-dimensional representation. This representation can then be decoded to generate new, often novel, molecules. However, chemists may question the ability of such a system to learn the actual chemistry necessary to generate drug-like molecules. One of the papers in this issue provides an investigation of this potential caveat by evaluating the scope of the actual chemistry learned by a generative model (Grebner et al.; DOI: 10.1021/acs.jmedchem.9b02044).

Perspectives. The Special Issue includes a number of important perspectives on the role of AI in drug discovery. One of these papers explores the broader theme of interactions between chemists and AI in depth (Griffen et al.; DOI: 10.1021/acs.jmedchem.0c00163), while another focuses on the impact of AI in synthesis (Struble et al.; DOI: 10.1021/acs.jmedchem.9b02120; vide supra). Many applications of AI in our field are still constrained by the limited availability of data, which is also addressed in a perspective discussing transfer learning methods that can be used to leverage knowledge gained in related projects to accelerate new efforts.

Practical Impact. We also note that only very few of the papers appearing in this Special Issue showcase real-world applications of AI that currently impact drug discovery. We are certainly encouraged by in-house evaluation of these methods by those on the front lines in pharma, as highlighted in one of the contributions (Rohall et al.; DOI: 10.1021/acs.jmedchem.9b02130; vide supra), and we emphasize the need for these methods to be put to the test in more “risky” scenarios. In particular, this means that AI applications, and especially predictive models, need to have “skin in the game” and directly influence the selection and prioritization of compounds in high-profile drug discovery programs. However, demonstrating the impact of AI at that level is still a rare event. Importantly, as long as “prospective” evaluations only consider the choices made by chemists (rather than the choices made by the algorithms), the real impact of these methods will be difficult to assess and, importantly, nearly impossible to improve. From this point of view, the field is still wide open to advance AI in drug discovery beyond the conceptual level and demonstrate the ability of smart algorithms to consistently design novel chemical matter going beyond a chemist’s imagination. To these ends, the contributions contained in this Special Issue are also considered as an encouragement to move forward “into the great wide open” (Tom Petty & the Heartbreakers, 1991). The papers certainly provide a realistic impression of the current state-of-the-art.

It is sincerely hoped that the readers of the Journal of Medicinal Chemistry will enjoy this Special Issue covering a hot topic in science from a drug discovery perspective.

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Cite this: J. Med. Chem. 2020, 63, 16, 8651–8652
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.jmedchem.0c01077
Published July 8, 2020

Copyright © 2020 American Chemical Society. This publication is available under these Terms of Use.

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