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LIMO: Latent Inceptionism for Targeted Molecule Generation
Peter Eckmann · Kunyang Sun · Bo Zhao · Mudong Feng · Michael Gilson · Rose Yu

Wed Jul 20 10:40 AM -- 10:45 AM (PDT) @ None

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 * 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.

Author Information

Peter Eckmann (University of California, San Diego)
Kunyang Sun (UCSD)
Bo Zhao (University of California, San Diego)
Mudong Feng (University of California, San Diego)
Michael Gilson (University of California, San Diego)
Rose Yu (University of California, San Diego)

Dr. Rose Yu is an assistant professor at the UC San Diego, Department of Computer Science and Engineering. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first principles with data-driven models. Among her awards, she has won Faculty Research Award from Facebook, Google, Amazon, and Adobe, several Best Paper Awards, and the Best Dissertation Award in USC.

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