| Abstract: |
Despite a Cambrian explosion in therapeutic modalities, small-molecule drugs remain a prominent and advantageous medical intervention. The universe of synthesizable, drug-like small molecules is astronomical. Given this scale, efficiently narrowing in on therapeutic candidates that are potent, selective, and tolerable cannot occur by happenstance. Over the past several decades, computational tools have become commonplace among pharma companies seeking to discover new small-molecule drugs. For example, molecular mechanics force fields are used to power molecular dynamics simulations-an effective approach for virtually screening and optimizing candidate molecules. In parallel, data-driven methods such as machine learning have supercharged the field's ability to design potentially bioactive compounds. Despite these advances, established computational methods still suffer from issues relating to throughput, accuracy, generalizability, or combinations thereof. We argue that a merger of these technologies is inevitable and desirable, allowing the strengths of each to address the weaknesses of the other. This fusion-in the form of neural network potentials (NNPs)-is an exciting frontier for small-molecule discovery and design. Ostensibly, NNPs enable a swift, accurate, and generalizable solution for researchers developing the next generation of small-molecule drugs. |
