BME Seminar- Generative AI for Molecules: Semi-Equivariant Flows, Sketchy Diffusion, and Quantum Ground States Abstract-Daniel Fridman

Abstract:
Generative AI has made tremendous strides over the last few years in a wide variety of fields, including text, images, audio, and video. In this talk, we discuss the use of Generative AI techniques in the realm of molecules, emphasizing the incorporation of invariances to transformation groups, and covering three applications. In the first, we show an approach to the problem of generating molecules which will bind to a particular receptor molecule, a problem with strong applications in drug design. We design specialized normalizing flows which respect the physical invariances inherent in the problem, through the use of semi-equivariant networks. In the second application, we show how to adapt diffusion models to deal with this same problem. In particular, we address the size disparity between the receptor and the generated molecule, which can be problematic for learning as the receptor can overwhelm the training; we do so by creating a small sketch of the receptor, dubbed a “virtual receptor”. In the final scenario, we address a fundamental problem with applications in chemistry, biochemistry and materials science: computing the quantum ground state of a molecule. We demonstrate an efficient method of solving the Electronic Schrodinger Equation by using a carefully designed antisymmetric normalizing flow to construct the wavefunction ansatz.