ReviewComputational and structural biotechnology journal2020
Machine learning applications in drug development.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.
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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
52 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review.Journal of biomolecular structure & dynamics · 2022Pooled it
- Pooled it
- Colombian Contributions Fighting Leishmaniasis: A Systematic Review on Antileishmanials Combined with Chemoinformatics Analysis.Molecules (Basel, Switzerland) · 2020Pooled it
- Identifying Potential BACE1 Inhibitors from the ChEMBL Database Using Machine Learning and Atomistic Simulation Approaches.ACS omega · 2026Article
- Machine Learning-Guided Repositioning of a SARS-CoV-2-Targeting Molecular Series as Cruzain Inhibitors.ChemMedChem · 2026Article
- The topology of molecular representations and its influence on machine learning performance.Journal of cheminformatics · 2025Article
- Predicting SARS-CoV-2-specific CD4Clinical and experimental medicine · 2025Article
- Machine Learning-Powered Optimization of a CHO Cell Cultivation Process.Biotechnology and bioengineering · 2025Article
- Article
- Development and Validation of an Electronic Health Record-Based Risk Assessment Tool for Hypoglycemia in Patients With Type 2 Diabetes Mellitus.Journal of diabetes science and technology · 2025Article
- In Silico Conotoxin Studies: Progress and Prospects.Molecules (Basel, Switzerland) · 2024Review
- Trends analysis and future study of the pharmaceutical industry field: a scoping review.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Article
- Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine.Pharmaceutics · 2024Review
- Deep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer's disease.Journal of pharmaceutical analysis · 2024Article
- Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms.Heliyon · 2024Article
- Computational Approaches: A New Frontier in Cancer Research.Combinatorial chemistry & high throughput screening · 2024Review
- In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin.Functional composite materials · 2024Article
- Synergies and Challenges in the Preclinical and Clinical Implementation of Pathology Artificial Intelligence Applications.Mayo Clinic proceedings. Digital health · 2023Review
- Advances in tissue engineering and biofabrication forBioprinting (Amsterdam, Netherlands) · 2023Article
- Comparative analysis between 2D and 3D colorectal cancer culture models for insights into cellular morphological and transcriptomic variations.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.
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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.