CGBVS‐DNN: Prediction of Compound‐protein Interactions Based on Deep Learning

Masatoshi Hamanaka, Kei Taneishi, Hiroaki Iwata, Jun Ye, Jianguo Pei, Jinlong Hou, Yasushi Okuno

Molecular Informatics 2016 article cited by 70

importance 6/10 solved Computational Drug Discovery Methods drug discovery stage: research

Problem

SVM-based compound-protein interaction prediction could not scale to training sets of more than a million CPIs due to exponential time and memory costs.

Why it matters

CPI prediction is the first step of in-silico drug screening, so scalability directly affects drug discovery speed and cost.

What the AI did

Replaced the SVM with a deep neural network (CGBVS-DNN) trained via mini-batches, enabling learning from millions of compound-protein pairs.

Results

Outperformed original CGBVS on a quarter million CPIs; cross-validation accuracy up to 98.2% with 4 million CPIs

Impact

Cited 70 times; enabled large-scale in-silico screening per reported benchmarks.

Record

Venue	Molecular Informatics
Published	2016
Work type	article
Authors	Masatoshi Hamanaka, Kei Taneishi, Hiroaki Iwata, Jun Ye, Jianguo Pei, Jinlong Hou, Yasushi Okuno
Citations	70 (OpenAlex, at import time)
References	18
Model or method	CGBVS-DNN
Organization	not stated in source
Primary source	https://doi.org/10.1002/minf.201600045
Open access	closed
Keywords	compound-protein interaction, deep neural network, virtual screening, drug design

Importance rating

6 / 10

significant — thousands to millions affected

Abstract calls CPI prediction the first step of in-silico drug screening; removing the >1M-pair scalability bottleneck aids drug discovery, but no patient or industry scale is quantified.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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