ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed.
- TPPS4 inhibits PEDV by stabilizing viral RNA G-quadruplex and promoting ER stress: a transfer-learning-driven discovery.Journal of virology · 2026Article
- Fast and Flexible 3D Molecular Design Framework for Novel Organic Optoelectronic Materials.JACS Au · 2026Article
- MolGAN-QRL: a hybrid framework for molecule generation using quantum-enhanced reinforcement learning.Journal of cheminformatics · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- A Small-Molecule Drug for the Self-Checking of Mitophagy.Angewandte Chemie (International ed. in English) · 2025Article
- Multi-dimensional data-driven computational drug repurposing strategy for screening novel neuroprotective agents in ischemic stroke.Theranostics · 2025Article
- From Small Data Modeling to Large Language Model Screening: A Dual-Strategy Framework for Materials Intelligent Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to 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.