ReviewChemical science2025
Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design.
Review in Chemical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Recent Advances in the Design of Inhibitors Targeting the Viral Entry and Replication of the SARS-CoV-2 Virus, Driven by In Silico Approaches.Molecules (Basel, Switzerland) · 2026Review
- AI-guided competitive docking for virtual screening and compound efficacy prediction.npj drug discovery · 2026Article
- Drug Discovery Strategies for Kallikrein-Related Peptidases.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional drug discovery suffers from high costs and low productivity, with compounds frequently failing due to insufficient efficacy or off-target binding. Structure-based approaches aim to address these challenges by directly incorporating protein target information during molecule design, potentially reducing late-stage failures. In this review, we focus on current deep learning methods for structure-based drug discovery. We discuss the range of approaches used to encode and utilise protein structural information, from early shape-based approaches to more recent co-folding models that predict protein and ligand structures as a single task. We aim to provide insight into how deep learning approaches that incorporate structural information can be used to design molecules with enhanced binding potential while maintaining chemical and physical plausibility and offer suggestions as to the future directions of the field.
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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.