ArticleJournal of cheminformatics2022
Transformer-based molecular optimization beyond matched molecular pairs.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Diversity Beats Size Scaling for Chemical Language Models.npj drug discovery · 2026Article
- An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.Journal of chemical information and modeling · 2026Article
- Autonomous bioisosteric replacement for multi-property optimization in drug design.Nature communications · 2026Article
- Modular Framework for 3D Molecular Generation in Computational Chemistry Applications.Journal of the American Chemical Society · 2026Article
- Unveiling the bioactive landscape of drug inactive ingredients (DIGs) using deep transfer learning.Acta pharmaceutica Sinica. B · 2026Article
- Unifying pJournal of chemical information and modeling · 2026Article
- Structural optimization of drug molecules with incrementally trained language models.Nature communications · 2026Article
- MolMod: a molecular modification platform for molecular property optimization via fragment-based generation.Molecular diversity · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Application of chemical similarity and bioisosteres to find allosteric inhibitors of type 2 lipid kinase γ.Scientific reports · 2025Article
- Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development.MedComm · 2025Review
- Comparison study of dominant molecular sequence representation based on diffusion model.Journal of computer-aided molecular design · 2025Article
- PepINVENT: generative peptide design beyond natural amino acids.Chemical science · 2025Article
- Developing muscarinic receptor M1 classification models utilizing transfer learning and generative AI techniques.Scientific reports · 2025Article
- Accelerating discovery of bioactive ligands with pharmacophore-informed generative models.Nature communications · 2025Article
- Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning.Communications chemistry · 2025Article
- GT-NMR: a novel graph transformer-based approach for accurate prediction of NMR chemical shifts.Journal of cheminformatics · 2024Article
- DrugAssist: a large language model for molecule optimization.Briefings in bioinformatics · 2024Article
- Exhaustive local chemical space exploration using a transformer model.Nature communications · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods have been proposed to solve this task by mimicking the chemist's intuition in terms of matched molecular pairs (MMPs). Although MMPs is a widely used strategy by medicinal chemists, it offers limited capability in terms of exploring the space of structural modifications, therefore does not cover the complete space of solutions. Often more general transformations beyond the nature of MMPs are feasible and/or necessary, e.g. simultaneous modifications of the starting molecule at different places including the core scaffold. This study aims to provide a general methodology that offers more general structural modifications beyond MMPs. In particular, the same Transformer architecture is trained on different datasets. These datasets consist of a set of molecular pairs which reflect different types of transformations. Beyond MMP transformation, datasets reflecting general structural changes are constructed from ChEMBL based on two approaches: Tanimoto similarity (allows for multiple modifications) and scaffold matching (allows for multiple modifications but keep the scaffold constant) respectively. We investigate how the model behavior can be altered by tailoring the dataset while using the same model architecture. Our results show that the models trained on differently prepared datasets transform a given starting molecule in a way that it reflects the nature of the dataset used for training the model. These models could complement each other and unlock the capability for the chemists to pursue different options for improving a starting molecule.
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