ArticlePatterns (New York, N.Y.)2024
Multi-objective latent space optimization of generative molecular design models.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- Generation of antifungals to combat drug resistance using language models and diffusion models.Journal of advanced research · 2026Article
- From Prediction to Creation: Generative Plant Design.Plants (Basel, Switzerland) · 2026Review
- Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.Pharmaceutics · 2026Review
- Artificial Intelligence in Molecular Optimization: Current Paradigms and Future Frontiers.International journal of molecular sciences · 2025Review
- Hybridization of SMILES and chemical-environment-aware tokens to improve performance of molecular structure generation.Scientific reports · 2025Article
- Accelerating discovery of bioactive ligands with pharmacophore-informed generative models.Nature communications · 2025Article
- Article
- Multi-objective latent space optimization of generative molecular design models.Patterns (New York, N.Y.) · 2024Article
- ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.ArXiv · 2023Article
- Computer-aided multi-objective optimization in small molecule discovery.Patterns (New York, N.Y.) · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecules with desired properties. While the efficacy of the initial model strongly depends on the training data, the sampling efficiency of the model for suggesting novel molecules with enhanced properties can be further enhanced via latent space optimization (LSO). In this paper, we propose a multi-objective LSO method that can significantly enhance the performance of generative molecular design (GMD). The proposed method adopts an iterative weighted retraining approach, where the respective weights of the molecules in the training data are determined by their Pareto efficiency. We demonstrate that our multi-objective GMD LSO method can significantly improve the performance of GMD for jointly optimizing multiple molecular properties.
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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 Socratic graph.