ArticleJournal of computer-aided molecular design2019
Convolutional neural network scoring and minimization in the D3R 2017 community challenge.
Article in Journal of computer-aided molecular design, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- High-Throughput, High-Quality: Benchmarking GNINA and AutoDock Vina for Precision Virtual Screening Workflow.Molecules (Basel, Switzerland) · 2025Article
- Virtual Screening, Molecular Dynamics, and Mechanism Study of Homeodomain-Interacting Protein Kinase 2 Inhibitor in Renal Fibroblasts.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Advances in machine-learning approaches to RNA-targeted drug design.Artificial intelligence chemistry · 2024Article
- AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection.Scientific reports · 2023Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023Review
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.Journal of chemical information and modeling · 2022Review
- Ultrahigh Throughput Protein-Ligand Docking with Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Virtual Screening with Gnina 1.0.Molecules (Basel, Switzerland) · 2021Article
- Deep Learning in Virtual Screening: Recent Applications and Developments.International journal of molecular sciences · 2021Review
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations.Chemical science · 2020Article
- Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4.Journal of computer-aided molecular design · 2020Article
- The role of human in the loop: lessons from D3R challenge 4.Journal of computer-aided molecular design · 2020Article
- D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.Journal of computer-aided molecular design · 2020Article
- Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4.Journal of computer-aided molecular design · 2020Article
- Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges.Polymers · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We assess the ability of our convolutional neural network (CNN)-based scoring functions to perform several common tasks in the domain of drug discovery. These include correctly identifying ligand poses near and far from the true binding mode when given a set of reference receptors and classifying ligands as active or inactive using structural information. We use the CNN to re-score or refine poses generated using a conventional scoring function, Autodock Vina, and compare the performance of each of these methods to using the conventional scoring function alone. Furthermore, we assess several ways of choosing appropriate reference receptors in the context of the D3R 2017 community benchmarking challenge. We find that our CNN scoring function outperforms Vina on most tasks without requiring manual inspection by a knowledgeable operator, but that the pose prediction target chosen for the challenge, Cathepsin S, was particularly challenging for de novo docking. However, the CNN provided best-in-class performance on several virtual screening tasks, underscoring the relevance of deep learning to the field of drug discovery.
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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.