ArticlePLoS computational biology2020
Deep Learning-driven research for drug discovery: Tackling Malaria.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- The role of artificial intelligence (AI) in the elimination of malaria: a systematic literature review.Malaria journal · 2025Pooled it
- Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum.Molecular diversity · 2022Pooled it
- Interpretable machine learning-driven identification of novel DENV NS2B-NS3 protease inhibitors through multi-stage virtual screening and experimental validation.Molecular diversity · 2026Article
- Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.Computational toxicology (Amsterdam, Netherlands) · 2026Article
- Hybrid Computational Framework Integrating Ensemble Learning, Molecular Docking, and Dynamics for Predicting Antimalarial Efficacy of Malaria Box Compounds.International journal of molecular sciences · 2026Article
- Addressing infectious diseases in Africa by accelerating drug discovery through data science.Communications medicine · 2025Review
- Employing machine learning for identifying antifungal compounds againstFuture microbiology · 2025Article
- Development and experimental validation of a machine learning model for the prediction of new antimalarials.BMC chemistry · 2025Article
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- Introducing CRAFT: The Center for Research and Advancement in Fragments and molecular Targets.ACS medicinal chemistry letters · 2024Article
- Innovative Multistage ML-QSAR Models for Malaria: From Data to Discovery.ACS medicinal chemistry letters · 2024Article
- Exploring Huntington's Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2023Review
- Molecular Descriptors Property Prediction Using Transformer-Based Approach.International journal of molecular sciences · 2023Article
- Roles of Virtual Screening and Molecular Dynamics Simulations in Discovering and Understanding Antimalarial Drugs.International journal of molecular sciences · 2023Review
- Prospects of Novel and Repurposed Immunomodulatory Drugs against Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19 Disease.Journal of personalized medicine · 2023Review
- Geographical classification of malaria parasites through applying machine learning to whole genome sequence data.Scientific reports · 2022Article
- Random Forest Model Predictions Afford Dual-Stage Antimalarial Agents.ACS infectious diseases · 2022Article
- Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.Frontiers in cellular and infection microbiology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
Abstract
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.
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.