ReviewMolecular diversity2022
Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.
Review in Molecular diversity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 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
65 citing papers in PubMed, 1 synthesis or guideline pooled it, 179 citations in OpenAlex.
- Transformative Role of Artificial Intelligence in Drug Discovery and Translational Medicine: Innovations, Challenges, and Future Prospects.Drug design, development and therapy · 2025Pooled it
- DeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction.Journal of computer-aided molecular design · 2026Article
- Deciphering the rules of regulated cell death subroutines by in silico methods.Journal of advanced research · 2026Review
- Image-guided activation of drugs with electromagnetic radiation.Nature chemical biology · 2026Review
- Current progress in pathway-targeted therapeutics for Alzheimer's disease: mechanistic insights and windows of opportunity.Molecular and cellular biochemistry · 2026Review
- Identification of potential MenT3 inhibitors for Mycobacterium tuberculosis using the generative artificial intelligence and SilicoXplore platform.Scientific reports · 2026Article
- XGBPred-ACSM: A Hybrid Descriptor-Driven XGBoost Framework for Anticancer Small Molecule Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Generative Artificial Intelligence Transitions Pharmaceutical Development from Empirical Screening to Predictive Molecular Design and Clinical Trial Optimization.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Machine Learning and Molecular Modeling for Drug Repurposing Targeting Potential PI3Kα Inhibitors in Post-CoViD-19 Pulmonary Fibrosis.ACS omega · 2026Article
- Artificial neural network as a strategy to predict rheological properties in emulgel formulations.Scientific reports · 2026Article
- Task-specific pre-training for molecular property prediction.Briefings in bioinformatics · 2026Article
- Computational Design of Drugs for Epilepsy using a Novel Guided Evolutionary Algorithm for Enhanced Blood Brain Barrier Permeability.Central nervous system agents in medicinal chemistry · 2026Article
- Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms.Computational and structural biotechnology journal · 2026Review
- The antimicrobial peptide EM86 loaded to gamma-irradiated sodium alginate/polyvinyl alcohol electrospun nanofibrous dressing treated multidrug-resistantFrontiers in bioengineering and biotechnology · 2026Article
- Digital health in clinical pharmacy practice: transforming precision medicine and pharmacometrics in Africa.Frontiers in pharmacology · 2026Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Clinical and MRI markers for acute vs chronic temporomandibular disorders using a machine learning and deep neural networks.Communications medicine · 2025Article
- Automated drug design for druggable target identification using integrated stacked autoencoder and hierarchically self-adaptive optimization.Scientific reports · 2025Article
- The Pharmaceutical Industry's Future: How Artificial Intelligence is Transforming Medicine.Advanced pharmaceutical bulletin · 2025Review
- The Use and Importance ofArtificial Intelligence in Vaccine Research, Development, and Production.Archives of Razi Institute · 2025Review
5 more citing papers are in PubMed but not listed here.
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 at 1 institution in 1 country.
Funding
Abstract
The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.
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.