ArticleScientific reports2024
AI is a viable alternative to high throughput screening: a 318-target study.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 68 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
68 citing papers in PubMed.
- Multiscale cross modal fusion and cascaded signal propagation for drug target interaction prediction.iScience · 2026Article
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- Structural and mechanistic insights into gating and allosteric modulation of GluN1-GluN3A NMDA receptors.Nature structural & molecular biology · 2026Article
- In Silico Isomerization Produces Apt Negative Data for VHTS Validation.Journal of chemical information and modeling · 2026Article
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- Identification of compounds that repress DUX4 expression in facioscapulohumeral muscular dystrophy.Scientific reports · 2026Article
- MAGC-DTI: modality-shared space and adaptive gated interactive cross-attention for drug-target interaction prediction.Scientific reports · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Identification of compounds that repress DUX4 expression in facioscapulohumeral muscular dystrophy.bioRxiv : the preprint server for biology · 2026Article
- From General-Purpose to Disease-Specific Features: Aligning LLM Embeddings on a Disease-Specific Biomedical Knowledge Graph for Drug Repurposing.bioRxiv : the preprint server for biology · 2026Article
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- Docking of millions: accelerating a million-scale virtual screening using deep learning.Briefings in bioinformatics · 2026Article
- Search for Specific Inhibitors Targeting Type IA Topoisomerases.Journal of molecular biology · 2026Review
- UCM-A86 is a selective positive allosteric modulator of GluN1/GluN3 NMDA receptors.Molecular pharmacology · 2026Article
- A Framework for Autonomous AI-Driven Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- Machine Learning in Preclinical Development of Antiviral Peptide Candidates: A Review of the Current Landscape.Viruses · 2026Review
- Novel Small-Molecule Analogues of IU1 Ameliorate Amyloid-β Mediated Toxicity in Alzheimer's Disease Cell and Worm Models.International journal of molecular sciences · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
8 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
1 author.
Funding
Abstract
High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.
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.