Machine-learning (ML) is an increasingly popular choice to identify bioactive, potent molecules in early-stage drug discovery. In structure-based approaches, ML can evaluate candidates by computationally generating and scoring co-complexes. Virtual, make-on-demand combinatorial chemical libraries provide a vast array of potential compounds for experimental validation; however, the sheer size of these libraries now exceeds trillions of molecules, making exhaustive searches impractical. To overcome this limitation, we present and compare two methods for traversal of virtual chemical libraries: Analog-Based Explorer (ABE) and Synthon-based Discovery (SbD).

ABE navigates the library by iteratively finding, scoring and selecting analogs. AtomNet®, a structure-based graph neural network trained on bioactivity measurements, is used to score based on predicted affinity for the target of interest. We incorporate fragment-based similarity search methods, including SpaceMACS, SpaceLight, and CSLVAE, to enable scaling to ultra-large libraries.

SbD is an alternative library exploration approach inspired by V-SYNTHES that uses AtomNet® to evaluate synthons individually, then combines favorable synthons together into products. We compared the performance of ABE and SbD and demonstrate how they complement each other in different contexts with predictions from different areas of chemical space.

To further optimize our search, we combined ABE and SbD in a serial manner, with the outputs of SbD fed into ABE for virtual hit expansion. Our results demonstrate the effectiveness of these methods in navigating virtual chemical libraries and identifying potential bioactive molecules, providing a valuable tool for early-stage drug discovery.

Illustration of ABE and SbD approaches

Aryan Pedawi

Atomwise

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