ArticleMethods in molecular biology (Clifton, N.J.)2022
Deep Learning in Structure-Based Drug Design.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Artificial intelligence-driven discovery of YH395A: A novel TGFβR1 inhibitor with potent anti-tumor activity against triple-negative breast cancer.Cell communication and signaling : CCS · 2025Article
- Accelerating COVID-19 Drug Discovery with High-Performance Computing.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics.Molecular aspects of medicine · 2023Review
- Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational methods play an increasingly important role in drug discovery. Structure-based drug design (SBDD), in particular, includes techniques that take into account the structure of the macromolecular target to predict compounds that are likely to establish optimal interactions with the binding site. The current interest in machine learning algorithms based on deep neural networks encouraged the application of deep learning to SBDD related problems. This chapter covers selected works in this active area of research.
Indexed as
Identifiers
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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.