Towards computationally efficient graph generative models for molecular search

At the American Chemical Society Spring National Meeting & Expo, Atomwise members were selected to present their research and work. Learn what our Atoms have been working on below and visit Atomwise at ACS Fall 2022 National Meeting for other presentation sessions. 

Aryan Pedawi, PhD 
Atomwise

Atomwise Co-Authors: Hossam Ashtawy, Brandon Anderson

Title: Towards computationally efficient graph generative models for molecular search

Division: Computers in Chemistry

Abstract

Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space. Recent years have witnessed rapid growth in these libraries from millions to trillions of compounds, hiding undiscovered, potent hits for a variety of therapeutic targets. However, they are quickly approaching a size beyond that which permits explicit enumeration, presenting new challenges for virtual screening. To overcome these challenges, we propose a new permutation-invariant graph variational autoencoder which represents such libraries as differentiable, hierarchically-organized databases. Given a compound from the library, the molecular encoder constructs a query for retrieval, which is utilized by the molecular decoder to reconstruct the compound by first decoding its chemical reaction and subsequently decoding its reactants. Our design minimizes autoregression in the decoder, facilitating the generation of large, valid molecular graphs. Our method performs fast and parallel batch inference for ultra-large synthesis libraries, enabling a number of important applications in early-stage drug discovery. Compounds proposed by our method are guaranteed to be in the library, and thus synthetically and cost-effectively accessible.

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