  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.
