## LIMO: Latent Inceptionism for Targeted Molecule Generation

### Peter Eckmann · Kunyang Sun · Bo Zhao · Mudong Feng · Michael Gilson · Rose Yu

##### Hall E #123

Keywords: [ DL: Generative Models and Autoencoders ] [ OPT: Sampling and Optimization ] [ APP: Health ] [ OPT: Multi-objective Optimization ] [ APP: Chemistry and Drug Discovery ]

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Wed 20 Jul 3:30 p.m. PDT — 5:30 p.m. PDT

Spotlight presentation: APP: Chemistry and Drug Discovery
Wed 20 Jul 10:15 a.m. PDT — 11:45 a.m. PDT

Abstract: Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physics-based methods. We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties. Comprehensive experiments show that LIMO performs competitively on benchmark tasks and markedly outperforms state-of-the-art techniques on the novel task of generating drug-like compounds with high binding affinity, reaching nanomolar range against two protein targets. We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted $K_D$ (a measure of binding affinity) of $6 \cdot 10^{-14}$ M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets. Code is available at https://github.com/Rose-STL-Lab/LIMO.

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