Over the past several decades, computational approaches to structure based drug discovery have advanced to the point where they are now capable of playing a major role in designing potent and selective compounds with real clinical potential. These methods span a range from empirical docking and scoring techniques (e.g. as embodied in the Glide program) to rigorous physics- based simulations (using free energy perturbation techniques) which can, under favorable conditions, predict protein-ligand binding affinities to an accuracy of roughly 1 kcal/mole.  We will discuss the current state of the art for these technologies, including recent novel methods development, assessments of performance using large retrospective data sets, and illustrative prospective applications in drug discovery projects. The question of how to best integrate computational methodology into projects, incorporating modern machine learning approaches as appropriate, will also be discussed.

Computational methods for structure based drug discovery

ACS Spring Meeting 2021

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