Drug Response Prediction
14 papers with code • 1 benchmarks • 2 datasets
Drug response prediction is about using computer methods to guess how someone will react to certain medicines. It involves looking at various types of data, like genes, drug structures, and medical records, to predict how well a person will respond to a particular treatment. The aim is to create personalized treatment plans for patients, ensuring they get the best results with the fewest side effects. This approach not only helps doctors choose the right medicines for each patient but also speeds up the development of new drugs by predicting their effectiveness and safety. Techniques like machine learning and deep learning are commonly used to make these predictions based on different types of data, such as genetics and medical history.
DrugBank
GDSC
Most implemented papers
Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening
drugd/msda
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In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening.
TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representation
Although many models have been developed to utilize the representations of drugs and cancer cell lines for predicting cancer drug responses (CDR), their performances can be improved by addressing issues such as insufficient data modality, suboptimal fusion algorithms, and poor generalizability for novel drugs or cell lines.
Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information
Cancer remains a global challenge due to its growing clinical and economic burden.
WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer
kyrs/wiser
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Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients.
