Accelerating Drug Discovery with Advanced Computational Modeling
, Schrödinger
Small molecule drug discovery requires identification of ligands manifesting highly potent and selective binding, while maintaining a wide variety of other ligand properties required for safety and biological efficacy. We will present how integrated deployment and collaborative use of advanced computational modeling and next-generation machine learning can enable discovery teams to rapidly achieve these goals. We'll also present multiple case studies from active drug discovery projects, highlighting how these technologies, rather than simply being used to evaluate ligand design ideas, can now be used to directly ideate novel ligand matter likely to advance discovery efforts. In addition to these case studies and examples from active drug discovery projects, we'll also review how improvements in high-performance cloud computing continue to open new possibilities for large-scale computationally driven molecular design.