Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
[PDF][PDF] Machine learning for multi-omics data integration in cancer
Multi-omics data analysis is an important aspect of cancer molecular biology studies and
has led to ground-breaking discoveries. Many efforts have been made to develop machine …
has led to ground-breaking discoveries. Many efforts have been made to develop machine …
GraphDTA: predicting drug–target binding affinity with graph neural networks
The development of new drugs is costly, time consuming and often accompanied with safety
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …
Artificial intelligence, machine learning, and drug repurposing in cancer
Z Tanoli, M Vähä-Koskela… - Expert opinion on drug …, 2021 - Taylor & Francis
Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs
for new medical indications. Several machine learning (ML) and artificial intelligence (AI) …
for new medical indications. Several machine learning (ML) and artificial intelligence (AI) …
Machine learning and feature selection for drug response prediction in precision oncology applications
M Ali, T Aittokallio - Biophysical reviews, 2019 - Springer
In-depth modeling of the complex interplay among multiple omics data measured from
cancer cell lines or patient tumors is providing new opportunities toward identification of …
cancer cell lines or patient tumors is providing new opportunities toward identification of …
Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects
We present comboFM, a machine learning framework for predicting the responses of drug
combinations in pre-clinical studies, such as those based on cell lines or patient-derived …
combinations in pre-clinical studies, such as those based on cell lines or patient-derived …
Gefa: early fusion approach in drug-target affinity prediction
Predicting the interaction between a compound and a target is crucial for rapid drug
repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) …
repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) …
SAG-DTA: prediction of drug–target affinity using self-attention graph network
The prediction of drug–target affinity (DTA) is a crucial step for drug screening and
discovery. In this study, a new graph-based prediction model named SAG-DTA (self …
discovery. In this study, a new graph-based prediction model named SAG-DTA (self …
DeepGLSTM: deep graph convolutional network and LSTM based approach for predicting drug-target binding affinity
S Mukherjee, M Ghosh, P Basuchowdhuri - Proceedings of the 2022 SIAM …, 2022 - SIAM
Abstract Development of new drugs is an expensive and time-consuming process. Due to
the world-wide SARS-CoV-2 outbreak, it is essential that new drugs for SARS-CoV-2 are …
the world-wide SARS-CoV-2 outbreak, it is essential that new drugs for SARS-CoV-2 are …
Medical decision support system for cancer treatment in precision medicine in developing countries
In many developing countries and regions, there are medical problems such as dense
populations, lack of medical resources, and shortage of doctors, making it impossible to …
populations, lack of medical resources, and shortage of doctors, making it impossible to …