ArticleFrontiers in pharmacology2020
Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery.
Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 2 of them syntheses 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
70 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- High-throughput screening and meta-analysis for lead compounds in antimalarial drug discovery.Malaria journal · 2025Pooled it
- Drug pricing models, no 'one-size-fits-all' approach: a systematic review and critical evaluation of pricing models in an evolving pharmaceutical landscape.The European journal of health economics : HEPAC : health economics in prevention and care · 2025Pooled it
- Population-pharmacokinetic/pharmacodynamic model of atractylodes lancea (Thunb.) DC. administration in patients with advanced-stage intrahepatic cholangiocarcinoma: a dosage prediction.BMC complementary medicine and therapies · 2024Trial
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- Liver Disease and Plant-Derived Phytoconstituents: From Ethnopharmacology to Modern Medicine.Phytotherapy research : PTR · 2026Review
- Contextual Representation Learning With ResNet Refinement and Deep Classification for Enhanced Drug Target Interaction Prediction.Chemical biology & drug design · 2026Article
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- Human organoids: Fit for drug discovery?Stem cell reports · 2026Review
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Novel molecular design via a scaffold-aware transformer with multi-scale attention mechanisms.Journal of cheminformatics · 2026Article
- HopWD-DTA: a novel framework for drug-target affinity prediction fusing multi-hop neighborhoods and deep features.Journal of molecular modeling · 2026Article
- New Strategies for the Prevention and Therapy of Alzheimer's Disease Based on Stimulation of Brain Drainage and Lymphatic Clearance.International journal of molecular sciences · 2026Review
- Article
- Ex situ heart perfusion: a novel model for drug validation and translation.Frontiers in cardiovascular medicine · 2026Article
- Identification of potential drug target genes for atopic dermatitis leveraging Mendelian randomization, single-cell transcriptomics, and spatial transcriptomics.Archives of medical science : AMS · 2026Article
- Neuro-lymphaphotonics opens new horizons of the future technologies for the therapy of brain diseases.Theranostics · 2026Review
- Repurposing clinically safe drugs for DNA repair pathway choice in CRISPR genome editing and synthetic lethality.Nature communications · 2025Article
- SGEDiff: a subgraph-enriched diffusion model for structure-based 3D molecular generation.Journal of cheminformatics · 2025Article
- VNFlow: integration of variational autoencoders and normalizing flows for novel molecular design.Journal of cheminformatics · 2025Article
- GatorAffinity: Boosting Protein-Ligand Binding Affinity Prediction with Large-Scale Synthetic Structural Data.bioRxiv : the preprint server for biology · 2025Article
10 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.
Funding
Abstract
Conventional drug discovery is long and costly, and suffers from high attrition rates, often leaving patients with limited or expensive treatment options. Recognizing the overwhelming need to accelerate this process and increase success, the ATOM consortium was formed by government, industry, and academic partners in October 2017. ATOM applies a team science and open-source approach to foster a paradigm shift in drug discovery. ATOM is developing and validating a precompetitive, preclinical, small molecule drug discovery platform that simultaneously optimizes pharmacokinetics, toxicity, protein-ligand interactions, systems-level models, molecular design, and novel compound generation. To achieve this, the ATOM Modeling Pipeline (AMPL) has been developed to enable advanced and emerging machine learning (ML) approaches to build models from diverse historical drug discovery data. This modular pipeline has been designed to couple with a generative algorithm that optimizes multiple parameters necessary for drug discovery. ATOM's approach is to consider the full pharmacology and therapeutic window of the drug concurrently, through computationally-driven design, thereby reducing the number of molecules that are selected for experimental validation. Here, we discuss the role of collaborative efforts such as consortia and public-private partnerships in accelerating cross disciplinary innovation and the development of open-source tools for drug discovery.
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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.