ArticlePharmaceuticals (Basel, Switzerland)2021
A Deep-Learning Proteomic-Scale Approach for Drug Design.
Article in Pharmaceuticals (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- Combining cutting edge computational and experimental methods for targeting KRAS mutations in non-small cell lung cancer.Expert opinion on drug discovery · 2026Review
- Multiscale analysis and optimal glioma therapeutic candidate discovery using the CANDO platform.Journal of cheminformatics · 2026Article
- Deciphering bisphenol A (BPA)-elicited osteoarthritis mechanisms through network toxicology and molecular docking, then de novo generation of novel therapeutic candidates.BMC pharmacology & toxicology · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.Bioinformatics (Oxford, England) · 2025Article
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.bioRxiv : the preprint server for biology · 2025Article
- Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development.MedComm · 2025Review
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer.Scientific reports · 2024Article
- Antiviral Approaches against Influenza Virus.Clinical microbiology reviews · 2023Review
- Multiscale Analysis and Validation of Effective Drug Combinations Targeting Driver KRAS Mutations in Non-Small Cell Lung Cancer.International journal of molecular sciences · 2023Article
- A Clinical Update on SARS-CoV-2: Pathology and Development of Potential Inhibitors.Current issues in molecular biology · 2023Review
- Optimal COVID-19 therapeutic candidate discovery using the CANDO platform.Frontiers in pharmacology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded "objective" signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (
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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.