ArticleFrontiers in pharmacology2019
Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods.
Article in Frontiers in pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- DockM8: an all-in-one open-source platform for consensus virtual screening in drug design.Journal of cheminformatics · 2026Article
- AI-driven fungicide design: From target identification to field application.Plant communications · 2026Review
- SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein-Ligand Interaction Modeling for METTL3.ACS omega · 2025Article
- Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers.Journal of advanced research · 2025Article
- Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds.Journal of chemical information and modeling · 2024Article
- Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint.ACS omega · 2024Article
- Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction.Biomolecules · 2024Article
- A practical guide to machine-learning scoring for structure-based virtual screening.Nature protocols · 2023Review
- SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation.Journal of advanced research · 2023Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons.Journal of chemical information and modeling · 2023Article
- Comparisons of artificial intelligence algorithms in automatic segmentation for fungal keratitis diagnosis by anterior segment images.Frontiers in neuroscience · 2023Article
- Novel computational pipelines in antiviral structure‑based drug design (Review).Biomedical reports · 2022Review
- Towards an Enrichment Optimization Algorithm (EOA)-based Target Specific Docking Functions for Virtual Screening.Molecular informatics · 2022Article
- Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.Journal of chemical information and modeling · 2022Review
- Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design.Frontiers in pharmacology · 2022Review
- Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2.Computational and structural biotechnology journal · 2022Review
- Discovery of a Potent Candidate for RET-Specific Non-Small-Cell Lung Cancer-A Combined In Silico and In Vitro Strategy.Pharmaceutics · 2021Article
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
No grant is acknowledged in the PubMed record.
Abstract
Scoring functions play an important role in structure-based virtual screening. It has been widely accepted that target-specific scoring functions (TSSFs) may achieve better performance compared with universal scoring functions in actual drug research and development processes. A method that can effectively construct TSSFs will be of great value to drug design and discovery. In this work, we proposed a deep learning-based model named DeepScore to achieve this goal. DeepScore adopted the form of PMF scoring function to calculate protein-ligand binding affinity. However, different from PMF scoring function, in DeepScore, the score for each protein-ligand atom pair was calculated using a feedforward neural network. Our model significantly outperformed Glide Gscore on validation data set DUD-E. The average ROC-AUC on 102 targets was 0.98. We also combined Gscore and DeepScore together using a consensus method and put forward a consensus model named DeepScoreCS. The comparison results showed that DeepScore outperformed other machine learning-based TSSFs building methods. Furthermore, we presented a strategy to visualize the prediction of DeepScore. All of these results clearly demonstrated that DeepScore would be a useful model in constructing TSSFs and represented a novel way incorporating deep learning and drug design.
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.