Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods
importance 8/10
ai assisted
Computational Drug Discovery Methods pharmaceutical R&D stage: research
Problem
Drug discovery and repositioning demand high resource investment and are time-consuming for the pharmaceutical industry.
Why it matters
High costs and long timelines burden pharmaceutical development of new therapies.
What the AI did
Reviews machine and deep learning algorithms used in virtual screening to select candidate molecules toward therapeutic targets in silico.
Results
not stated in source
Impact
Review describes ML/DL contributions to drug design; no measured impact stated.
Record
| Venue | Drugs and Drug Candidates |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Tiago Alves de Oliveira, Michel Pires da Silva, Eduardo Habib Bechelane Maia, Alisson Marques da Silva, Alex Gutterres Taranto |
| Citations | 136 (OpenAlex, at import time) |
| References | 157 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/ddc2020017 |
| Open access | gold |
| Keywords | virtual screening, drug discovery, machine learning, deep learning, computer-aided drug design |
Importance rating
8
major — millions of lives, an industry or a field
Drug discovery/repositioning costs and timelines burden an entire industry and delay new therapies for millions; abstract gives no quantified scale.