Natural Language Processing for Drug Discovery: The State of Practices, Opportunities, and Challenges
Hit identification is a crucial step in the drug discovery process, where potential drug candidates are identified from a large chemical space. There are several methods for hit identification, including structure and ligand based-virtual screening, fragment-based drug design, and AI-driven drug design approaches.
In the context of de novo drug design, both generative methods and NLP are used to extract the information about existing data points, generate and optimize molecular structures based on desired properties. These algorithms can be trained on a variety of data sources, including molecular data, its biological profile, leading to a more informed hit generation process. In this talk, I will share an overview of these methods, practices, limitations, and case studies.
Suneel Kumar BVS
Director of AI & Drug Design at Molecular Forecaster
Suneel Kumar BVS, Ph.D., Director of Computational Chemistry and AI, Molecular Forecaster, Montreal, Canada. Suneel has more than 18 years of industry experience and led many successful drug discovery projects to preclinical and clinical stages.
His research interests focus on Computer aided drug designing, natural language processing (NLP), and AI/ML methodologies and applications in Drug discovery and development.