<p>Inspired by ChatGPT’s popularity and wondering if this approach could speed up the drug design process, scientists in the <a href="https://www.chapman.edu/scst/index.aspx">Schmid College of Science and Technology</a> at Chapman University in Orange, California, decided to create their own genAI model, detailed in the new paper, “De Novo Drug Design using Transformer-based Machine Translation and Reinforcement Learning of Adaptive Monte-Carlo Tree Search,” to be published in the journal <em>Pharmaceuticals</em>. Dony Ang, Cyril Rakovski, and Hagop Atamian coded a model to learn a massive dataset of known chemicals, how they bind to target proteins, and the rules and syntax of chemical structure and properties writ large. </p>
<p>The end result can generate countless unique molecular structures that follow essential chemical and biological constraints and effectively bind to their targets — promising to vastly accelerate the process of identifying viable drug candidates for a wide range of diseases, at a fraction of the cost.</p>