ArticleJournal of cheminformatics2023
DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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Who cites it
39 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
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- QSAR-ML- and Metadynamics-Guided Design of Symmetrical Bis-Indanones to Overcome Mutational Anchor Loss in Acetylcholinesterase.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- Computer-aided structural modeling and drug discovery for G-protein-coupled receptors in the age of artificial intelligence.Current opinion in structural biology · 2026Review
- Scaffold-based evaluation metrics for fair comparison of molecular generators.Journal of cheminformatics · 2026Article
- MolMod: a molecular modification platform for molecular property optimization via fragment-based generation.Molecular diversity · 2026Article
- A large language model-guided reinforcement learning framework for EGFR anticancer drug design.Journal of computer-aided molecular design · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Recent advances in the pharmacology of voltage-gated ion channels.Pharmacological reviews · 2025Review
- Artificial intelligence in bioinformatics: a survey.Briefings in bioinformatics · 2025Review
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Reverse engineering molecules from fingerprints through deterministic enumeration and generative models.Journal of cheminformatics · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products.Natural products and bioprospecting · 2025Review
- Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design.Journal of cheminformatics · 2025Article
- Article
- Computational Landscape in Drug Discovery: From AI/ML Models to Translational Application.Scientifica · 2025Review
- Status and Prospects of Research on Deep Learning-basedCurrent computer-aided drug design · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. With the rapid growth of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work we proposed a method named DrugEx, which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives and does not allow users to input any prior information (i.e. a desired scaffold). In order to improve the general applicability, we updated DrugEx to design drug molecules based on scaffolds which consist of multiple fragments provided by users. Here, a Transformer model was employed to generate molecular structures. The Transformer is a multi-head self-attention deep learning model containing an encoder to receive scaffolds as input and a decoder to generate molecules as output. In order to deal with the graph representation of molecules a novel positional encoding for each atom and bond based on an adjacency matrix was proposed, extending the architecture of the Transformer. The graph Transformer model contains growing and connecting procedures for molecule generation starting from a given scaffold based on fragments. Moreover, the generator was trained under a reinforcement learning framework to increase the number of desired ligands. As a proof of concept, the method was applied to design ligands for the adenosine A
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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 OpenQuestion graph.