Applications of machine learning in computer-aided drug discovery
importance 8/10
ai assisted
Computational Drug Discovery Methods drug discovery stage: research
Problem
Drug discovery needs predictive models for de novo drug design, binding site prediction, and binding affinity of small molecules.
Why it matters
The abstract states ML has revolutionised structure-based drug design by informing the drug discovery process.
What the AI did
This is a review summarising recent deep learning trends in structure-based drug design across three application areas.
Results
not stated in source
Impact
Review only; summarises the field, presents no new experimental findings or benchmarks.
Record
| Venue | QRB Discovery |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | SM Bargeen Alam Turzo, Eric R. Hantz, Steffen Lindert |
| Citations | 27 (OpenAlex, at import time) |
| References | 187 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1017/qrd.2022.12 |
| Open access | gold |
| Keywords | machine learning, deep learning, drug discovery, binding affinity, de novo design |
Importance rating
8
major — millions of lives, an industry or a field
Drug discovery is a foundational field with industry-wide impact; abstract says ML revolutionised SBDD and informs drug discovery, but gives no scale.