ArticleJournal of chemical information and modeling2020
Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks.
Article in Journal of chemical information and modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Integrating Machine Learning into Free Energy Perturbation Workflows.Journal of chemical information and modeling · 2025Review
- Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.Computational and structural biotechnology journal · 2024Review
- Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Chemical Space Exploration with Active Learning and Alchemical Free Energies.Journal of chemical theory and computation · 2022Article
- Predicting Protein-Ligand Docking Structure with Graph Neural Network.Journal of chemical information and modeling · 2022Review
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- Article
- The impact of cross-docked poses on performance of machine learning classifier for protein-ligand binding pose prediction.Journal of cheminformatics · 2021Article
- GNINA 1.0: molecular docking with deep learning.Journal of cheminformatics · 2021Article
- Pepsin-like aspartic proteases (PAPs) as model systems for combining biomolecular simulation with biophysical experiments.RSC advances · 2021Review
- A Potential Off-Target Effect of the Wnt/β-Catenin Inhibitor KYA1797K: PD-L1 Binding and Checkpoint Inhibition.Biomedicine hubArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The high-performance computational techniques have brought significant benefits for drug discovery efforts in recent decades. One of the most challenging problems in drug discovery is the protein-ligand binding pose prediction. To predict the most stable structure of the complex, the performance of conventional structure-based molecular docking methods heavily depends on the accuracy of scoring or energy functions (as an approximation of affinity) for each pose of the protein-ligand docking complex to effectively guide the search in an exponentially large solution space. However, due to the heterogeneity of molecular structures, the existing scoring calculation methods are either tailored to a particular data set or fail to exhibit high accuracy. In this paper, we propose a convolutional neural network (CNN)-based model that learns to predict the stability factor of the protein-ligand complex and exhibits the ability of CNNs to improve the existing docking software. Evaluated results on PDBbind data set indicate that our approach reduces the execution time of the traditional docking-based method while improving the accuracy. Our code, experiment scripts, and pretrained models are available at https://github.com/j9650/MedusaNet.
Indexed as
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.