Hybrid of the above

First, an ultra-large library of ~4.5B purchasable molecules from Enamine REAL will be parameterized and prepared using our own AI-accelerated quantum-mechanical methods to prepare it for subsequent structure-based automated virtual screening. We will then use our DeepDocking method to rapidly filter the library for top-scoring molecules, utilizing a funnelling approach that we have prospectively validated against SARS-CoV-2 main protease: the initial library will be first screened using DeepDocking-accelerated Glide; the identified molecules will be then screened using Autodock-GPU, and the same process will be then used for ICM docking. In this way, we will identify prospective consensus scoring molecules, providing a set of candidates that is highly enriched with active compounds. Our in-house pipeline will be then used to prioritize compounds for the next refinement step. The best candidates from DeepDocking will be subject to accurate molecular dynamics (MD) and relative free energy (RBFE) simulations, which in turn will be included as training data within an Active Learning (AL) cycle, thus the choice of the next generation of molecules for free energy calculations will be AI-driven in the subsequent loops. For representative molecules from scaffold-clustered diverse set an absolute binding free energy is accessed by MD simulation and used as an initial reference point for relative binding free energies computations with reinforcement learning-directed de novo generative models. Ultimately, top hits will be triaged and prioritised according to predicted binding affinity and combination of physical and ADME/tox properties as predicted by the panel of in-house ML methods.

DeepDocking, neural network atomistic force fields, molecular dynamics, thermodynamic integration, free energy methods, de novo generative models

Glide, ICM, OpenEye toolkit, AMBER

OpenChem, torchani, pytorch, Autodock-GPU

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