ReviewMolecular therapy. Nucleic acids2024
The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.
Review in Molecular therapy. Nucleic acids, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- CRISPR-Cas9-mediated therapeutics: Current clinical trials and therapy approval landscape to treat human diseases.Molecular therapy. Nucleic acids · 2026Review
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.Drug metabolism and disposition: the biological fate of chemicals · 2026Review
- Big Data for Drug Discovery: Some Historical Landscape, Considerations, and Applications for a Medicinal Chemist.ACS omega · 2026Review
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- 3D CNN-CVAE and LSTM-guided de novo design of rotigotine derivatives targeting dopamine D1 and D3 receptors in restless legs syndrome.PloS one · 2026Article
- Integrating machine learning, deep learning, and docking to predict aristolochic acid A carcinogenesis.Frontiers in pharmacology · 2026Article
- Artificial intelligence in idiopathic pulmonary fibrosis: advances, challenges and future directions.The European respiratory journal · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Potential role of different animal models for the evaluation of bioactive compounds.Annals of medicine and surgery (2012) · 2025Review
- Unlocking the potential: multimodal AI in biotechnology and digital medicine-economic impact and ethical challenges.NPJ digital medicine · 2025Review
- Integrating Machine Learning into Free Energy Perturbation Workflows.Journal of chemical information and modeling · 2025Review
- Integrating AI, Machine Learning, and Animal Models for Precision Oncology: Bridging Preclinical and Clinical Gaps.ACS pharmacology & translational science · 2025Article
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- Article
- Integrative Approaches to Myopathies and Muscular Dystrophies: Molecular Mechanisms, Diagnostics, and Future Therapies.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of
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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.