Generative network complex (GNC) for drug discovery

TL;DR

This paper introduces a comprehensive generative network complex (GNC) platform that designs, predicts properties, and evaluates novel drug-like compounds efficiently, covering large chemical spaces and integrating multiple deep learning models.

Contribution

The paper presents a novel integrated GNC platform combining generative, predictive, and evaluative deep learning models for accelerated drug discovery.

Findings

Generated over 2 million novel compounds for specific targets.

Successfully predicted 3D poses and evaluated druggability.

Completed the entire process in less than one week using supercomputers.

Abstract

It remains a challenging task to generate a vast variety of novel compounds with desirable pharmacological properties. In this work, a generative network complex (GNC) is proposed as a new platform for designing novel compounds, predicting their physical and chemical properties, and selecting potential drug candidates that fulfill various druggable criteria such as binding affinity, solubility, partition coefficient, etc. We combine a SMILES string generator, which consists of an encoder, a drug-property controlled or regulated latent space, and a decoder, with verification deep neural networks, a target-specific three-dimensional (3D) pose generator, and mathematical deep learning networks to generate new compounds, predict their drug properties, construct 3D poses associated with target proteins, and reevaluate druggability, respectively. New compounds were generated in the latent space by either randomized output, controlled output, or optimized output. In our demonstration, 2.08 million and 2.8 million novel compounds are generated respectively for Cathepsin S and BACE targets. These new compounds are very different from the seeds and cover a larger chemical space. For potentially active compounds, their 3D poses are generated using a state-of-the-art method. The resulting 3D complexes are further evaluated for druggability by a championing deep learning algorithm based on algebraic topology, differential geometry, and algebraic graph theories. Performed on supercomputers, the whole process took less than one week. Therefore, our GNC is an efficient new paradigm for discovering new drug candidates.