Reviewnpj drug discovery2025
Recent advances in molecular representation methods and their applications in scaffold hopping.
Review in npj drug discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions.Journal of computer-aided molecular design · 2026Review
- Task-adaptive multimodal molecular representations for structure-sensitive property prediction.Chemical science · 2026Article
- DFRL-Mol: a dual-stage framework of reinforcement learning for multi-scenario molecule optimization.Briefings in bioinformatics · 2026Article
- Scaffold hopping of furans into 3-cyanopyridines.Nature communications · 2026Article
- Exploring the chemical space of transition-metal cluster thermodynamics via automated first-principles calculations and machine learning.Nature communications · 2026Article
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Multimodal feature fusion for molecular property classification.Journal of cheminformatics · 2026Article
- ChemBang: Expanding the Chemical Space Around Small Molecules.Molecular informatics · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Learned Conformational Space and Pharmacophore Into Molecular Foundational Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Smart control of CAR-T cells: emerging strategies for safer and more effective cancer immunotherapy.Frontiers in immunology · 2026Review
- Scaling Biomedical Text-Mining: Transformers, GenAI, and Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.Research (Washington, D.C.) · 2026Review
- Development of Prediction Capabilities for High-Throughput Screening of Physiochemical Properties by Biomimetic Chromatography.Molecules (Basel, Switzerland) · 2025Review
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
The rapid evolution of molecular representation methods has significantly advanced the drug discovery process. Advances in language models, graph-based representations, and novel learning strategies have greatly improved the ability to characterize molecules. These AI-driven strategies extend beyond traditional structural data, facilitating exploration of broader chemical spaces and accelerating scaffold hopping. This review summarizes key advancements, discusses their advantages over conventional techniques, and highlights challenges in data quality and real-world applications.
Identifiers
What 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.