Опубликована: Июль 20, 2023

As cancer remains resistant to several modes of treatment, novel therapeutics are still under active investigation overcome treatment inefficacy in cancer. Given the high attrition rate de novo drug discovery, screening, and repurposing have offered time- cost-effective alternative strategies for identification potentially effective therapeutics. In contrast large-scale screens, computational approaches leverage increasing amounts biomedical data predict candidate therapeutic agents prior testing biological models. Current studies therapy prediction increasingly focused on combination therapies, as therapies numerous advantages over monotherapies. These include increased effect from synergistic interactions, reduced toxicity lowered doses, a risk resistance due multiple non-overlapping mechanisms action. This review provides summary classes methods used research, including networks, regression-based machine learning, classifier learning models, deep approaches, with goal presenting current progress field, particularly non-computational biologists. We conclude by discussing need further advancements technologies that incorporate disease mechanisms, characteristics, multi-omics data, clinical considerations generate patient-specific combinations, holistic integration will inevitably result optimal targeted

Язык: Английский