A knowledge-driven framework for predicting single-cell responses for unprofiled drugs
Ranking
Overall
84
Content
100
Popularity
46
Observed public metrics from 1 member.
Merged summary
TL;DR - Feng et al. introduce MAP, a knowledge-driven AI framework for predicting single-cell responses to previously unprofiled drugs. Integrating biological mechanism knowledge improves generalization to untested compounds and supports virtual screening for cancer drug candidates.
- Predicts cellular responses to chemical perturbations at single-cell resolution.
- Incorporates biological mechanism knowledge rather than relying solely on observed perturbation data.
- Targets out-of-distribution generalization to drugs not profiled during training.
- Demonstrates potential for prioritizing cancer drug candidates through virtual screening.
Sources (1)
A knowledge-driven framework for predicting single-cell responses for unprofiled drugs
Public signals
OpenAlex citations 0
TL;DR - Feng et al. introduce MAP, a knowledge-driven AI framework for predicting single-cell responses to previously unprofiled drugs. Integrating biological mechanism knowledge improves generalization to untested compounds and supports virtual screening for cancer drug candidates.
- Predicts cellular responses to chemical perturbations at single-cell resolution.
- Incorporates biological mechanism knowledge rather than relying solely on observed perturbation data.
- Targets out-of-distribution generalization to drugs not profiled during training.
- Demonstrates potential for prioritizing cancer drug candidates through virtual screening.