ArticleFuture science OA2021
Compound dataset and custom code for deep generative multi-target compound design.
Article in Future science OA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
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Who cites it
3 citing papers in PubMed.
- Fine-tuning of a generative neural network for designing multi-target compounds.Journal of computer-aided molecular design · 2022Article
- Compound dataset and custom code for deep generative multi-target compound design.Future science OA · 2021Article
- Binding mode analysis of ABCA7 for the prediction of novel Alzheimer's disease therapeutics.Computational and structural biotechnology journal · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimGenerating a data and software infrastructure for evaluating multi-target compound (MT-CPD) design via deep generative modeling. METHODOLOGY: The REINVENT 2.0 approach for generative modeling was extended for MT-CPD design and a large benchmark data set was curated. EXEMPLARY RESULTS & DATA: Proof-of-concept for deep generative MT-CPD design was established. Custom code and the benchmark set comprising 2809 MT-CPDs, 61,928 single-target and 295,395 inactive compounds from biological screens are made freely available. LIMITATIONS & NEXT STEPS: MT-CPD design via deep learning is still at its conceptual stages. It will be required to demonstrate experimental impact. The data and software we provide enable further investigation of MT-CPD design and generation of candidate molecules for experimental programs.
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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.