ReviewInternational journal of molecular sciences2021
Comprehensive Survey of Recent Drug Discovery Using Deep Learning.
Review in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.International journal of molecular sciences · 2023Pooled it
- Censoring chemical data to mitigate dual use risk.Digital discovery · 2026Article
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Contrastive learning in both structure and function spaces improve drug-target interaction prediction.BMC bioinformatics · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- DTBA-net: Drug-Target Binding Affinity prediction using feature selection in hybrid CNN model.Journal of computer-aided molecular design · 2025Article
- Application of 3D atom pair map in an attention model for enhanced drug virtual screening.Journal of cheminformatics · 2025Article
- Identification of Potential Inhibitors Targeting Non-Structural Proteins NS3 and NS5 of Dengue Virus Using Docking and Deep Learning Approaches.Pharmaceuticals (Basel, Switzerland) · 2025Article
- GramSeq-DTA: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information.Biomolecules · 2025Article
- AI-powered drug discovery for neglected diseases: accelerating public health solutions in the developing world.Journal of global health · 2025Article
- Investigation ofIn silico pharmacology · 2025Article
- Computational Landscape in Drug Discovery: From AI/ML Models to Translational Application.Scientifica · 2025Review
- Computational-aided drug design strategies for drug discovery and development against oral diseases.Frontiers in pharmacology · 2025Review
- DrugPipe: Generative artificial intelligence-assisted virtual screening pipeline for generalizable and efficient drug repurposing.Biology methods & protocols · 2025Article
- Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling.Journal of chemical information and modeling · 2024Article
- Causal enhanced drug-target interaction prediction based on graph generation and multi-source information fusion.Bioinformatics (Oxford, England) · 2024Article
- Drug-Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset.Journal of chemical information and modeling · 2024Article
- Deep learning algorithms applied to computational chemistry.Molecular diversity · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug-target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.
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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.