ReviewIEEE transactions on computational biology and bioinformatics
AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.
Review in IEEE transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- A Paradigm Shift in Snakebite Envenoming Therapy: From Conventional Antivenoms to Rationally Designed, Broadly Neutralizing Combination Therapies.ACS pharmacology & translational science · 2026Review
- Determination of bioactive and anti-inflammatory molecules of Thymbra spicata L. from Mardin by GC-MS and LC-Orbitrap HRMS: a DFT, molecular docking, ADMET, biological target and activity study.BMC complementary medicine and therapies · 2025Article
- Exploring Novel Inhibitory Compounds Against Phosphatase Gamma 2: A Therapeutic Target for Male Contraceptives.Current issues in molecular biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancements in AI applications within early drug discovery and post-market drug assessment. It addresses the identification and prioritization of new therapeutic targets, prediction of drug-target interaction (DTI), design of novel drug-like molecules, and assessment of the clinical efficacy of new medications. By integrating AI technologies, pharmaceutical companies can accelerate the discovery of new treatments, enhance the precision of drug development, and bring more effective therapies to market. This shift represents a significant move towards more efficient and cost-effective methodologies in the DDD landscape.
Indexed as
Identifiers
What Socratic 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 Socratic graph.