ArticleJournal of cheminformatics2019
DLIGAND2: an improved knowledge-based energy function for protein-ligand interactions using the distance-scaled, finite, ideal-gas reference state.
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Integrative Computational Chemistry Approaches in Modern Drug Discovery: Advances in Docking, Pharmacophore Modeling, Molecular Dynamics, and Virtual Screening.Pharmaceutics · 2026Review
- SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein-Ligand Interaction Modeling for METTL3.ACS omega · 2025Article
- iScore: A ML-Based Scoring Function for De Novo Drug Discovery.Journal of chemical information and modeling · 2025Article
- β-Lactoglobulin Enhances Clay and Activated Carbon Binding and Protection Properties for Cadmium and Lead.Industrial & engineering chemistry research · 2024Article
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening.Journal of chemical theory and computation · 2024Article
- Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK.Methods in molecular biology (Clifton, N.J.) · 2024Article
- The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks.International journal of molecular sciences · 2023Article
- Development of Antiepileptic Drugs throughout History: From Serendipity to Artificial Intelligence.Biomedicines · 2023Review
- VSTH: a user-friendly web server for structure-based virtual screening on Tianhe-2.Bioinformatics (Oxford, England) · 2023Article
- A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces.International journal of molecular sciences · 2022Article
- Protocol for iterative optimization of modified peptides bound to protein targets.Journal of computer-aided molecular design · 2022Article
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- Sfcnn: a novel scoring function based on 3D convolutional neural network for accurate and stable protein-ligand affinity prediction.BMC bioinformatics · 2022Article
- Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets.Frontiers in pharmacology · 2022Article
- AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.Journal of chemical information and modeling · 2021Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Establishing an Analogue Based In Silico Pipeline in the Pursuit of Novel Inhibitory Scaffolds against the SARS Coronavirus 2 Papain-Like Protease.Molecules (Basel, Switzerland) · 2021Article
- DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity.Bioinformatics and biology insights · 2021Article
- Novel Small-Molecule Scaffolds as Candidates against the SARS Coronavirus 2 Main Protease: A Fragment-Guided in Silico Approach.Molecules (Basel, Switzerland) · 2020Article
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9 authors.
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Abstract
Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based potentials derived from known three-dimensional structures of proteins and/or protein-ligand complex structures. This study seeks to improve a knowledge-based protein-ligand potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free statistical potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at https://github.com/sysu-yanglab/DLIGAND2 .
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