ArticleProceedings of machine learning research2022
LIMO: Latent Inceptionism for Targeted Molecule Generation.
Article in Proceedings of machine learning research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 17 citations in OpenAlex.
- Predicting molecular structure from sparse NMR data, proof-of-concept using a graph neural network.Digital discovery · 2026Article
- Learning earthquake ground motions via conditional generative modeling.Nature communications · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Sample-efficient generative molecular design using memory manipulation.Nature machine intelligence · 2026Article
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ACS central science · 2025Article
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ArXiv · 2025Article
- CMOMO: a deep multi-objective optimization framework for constrained molecular multi-property optimization.Briefings in bioinformatics · 2025Article
- Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- An open-source family of large encoder-decoder foundation models for chemistry.Communications chemistry · 2025Article
- Artificial Intelligence in Molecular Optimization: Current Paradigms and Future Frontiers.International journal of molecular sciences · 2025Review
- Using GNN property predictors as molecule generators.Nature communications · 2025Article
- Activity cliff-aware reinforcement learning for de novo drug design.Journal of cheminformatics · 2025Article
- Enhancing Unconditional Molecule Generation via Online Knowledge Distillation of Scaffolds.Molecules (Basel, Switzerland) · 2025Article
- DrugDiff: small molecule diffusion model with flexible guidance towards molecular properties.Journal of cheminformatics · 2025Article
- Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations.Journal of chemical theory and computation · 2024Article
- Hamiltonian diversity: effectively measuring molecular diversity by shortest Hamiltonian circuits.Journal of cheminformatics · 2024Article
- Recent advances in the self-referencing embedded strings (SELFIES) library.Digital discovery · 2023Review
- Application of Computational Biology and Artificial Intelligence in Drug Design.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
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
Identifiers
36193121PMC9527083W4283331738What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.