Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)­emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

The discovery of novel ligand chemotypes allows to explore uncharted regions in chemical space, thereby potentially improving synthetic accessibility, potency, and the drug-likeness of molecules. Here, we demonstrate the scaffold-hopping ability of the new Weighted Holistic Atom Localization and Entity Shape (WHALES) molecular descriptors compared to seven state-of-the-art molecular representations on 30,000 compounds and 182 biological targets. In a prospective application, we apply WHALES to the discovery of novel retinoid X receptor (RXR) modulators. WHALES descriptors identified four agonists with innovative molecular scaffolds, populating uncharted regions of the chemical space. One of the agonists, possessing a rare non-acidic chemotype, revealed high selectivity on 12 nuclear receptors and comparable efficacy as bexarotene on induction of ATP-binding cassette transporter A1, angiopoietin like protein 4 and apolipoprotein E. The outcome of this research supports WHALES as an innovative tool to explore novel regions of the chemical space and to detect novel bioactive chemotypes by straightforward similarity searching.

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Molecular shape and pharmacological function are interconnected. To capture shape, the fractal dimensionality concept was employed, providing a natural similarity measure for the virtual screening of de novo generated small molecules mimicking the structurally complex natural product (−)‐englerin A. Two of the top‐ranking designs were synthesized and tested for their ability to modulate transient receptor potential (TRP) cation channels which are cellular targets of (−)‐englerin A. Intracellular calcium assays and electrophysiological whole‐cell measurements of TRPC4 and TRPM8 channels revealed potent inhibitory effects of one of the computer‐generated compounds. Four derivatives of this identified hit compound had comparable effects on TRPC4 and TRPM8. The results of this study corroborate the use of fractal dimensionality as an innovative shape‐based molecular representation for molecular scaffold‐hopping.

Increases in computational power have contributed to growing interest in using modeling and simulation (M&S) to better understand and address blast injury for US service members. The development of an M&S capability that can comprehensively simulate human injury, lethality, and impairment due to blast injury threats in the military environment requires a large, coordinated integration of many models and simulations. This contribution describes how various lessons learned from the defense M&S domain were used to support the future development of an envisioned Modeling Capability for blast injury by providing simulation functionality for simulation-based experimentation. It first addresses conceptually the interoperability challenges when more than one simulation can be or must be applied and possibly composed for the experiment. This leads to the development of a proposed concept of operations for the application of the Modeling Capability and the development of a framework of services needed to allow the identification of an applicable simulation solution, selecting the subset of those simulations, composing them for the experiment, and assessing the results. As a nascent endeavor, anticipated challenges for implementing these concepts are discussed, leveraging lessons learned regarding interoperability, composability, use of services, repositories, and development of simulation compositions to conduct simulation-based experiments.

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