Add to ORKGDeciding when to stop: efficient experimentation to learn to predict drug-target interaction
The role of computational tools in the drug discovery and development process is becoming central, t...
An expanded chemical space is essential for improved identification of small molecules for emerging ...
Prediction of novel drug indications using network driven biological data prioritization and integra...
BACKGROUND: Active learning is a powerful tool for guiding an experimentation process. Instead of do...
Identifying potential and druggable targets for developing new drugs is the first major step for cur...
Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as c...
International audienceProtein-protein interactions (PPIs) may represent one of the next major classe...
The pharmaceutical industry relies on numerous well-designed experiments involving high-throughput t...
A number of supervised machine learning models have recently been introduced for the prediction of d...
The pharmaceutical industry is facing unprecedented pressure to increase its productivity. Attrition...
As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients...
Whereas drugs are intended to be selective, at least some bind to several physiologic targets, expla...
In this dissertation, I present several strategies to leverage experimental data towards a quantitat...
Drug research and development is the driving force of the pharmaceutical industry, and it has also c...
Two problems now threaten the future of anticancer drug development: (i) the information explosion h...
The role of computational tools in the drug discovery and development process is becoming central, t...
An expanded chemical space is essential for improved identification of small molecules for emerging ...
Prediction of novel drug indications using network driven biological data prioritization and integra...
BACKGROUND: Active learning is a powerful tool for guiding an experimentation process. Instead of do...
Identifying potential and druggable targets for developing new drugs is the first major step for cur...
Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as c...
International audienceProtein-protein interactions (PPIs) may represent one of the next major classe...
The pharmaceutical industry relies on numerous well-designed experiments involving high-throughput t...
A number of supervised machine learning models have recently been introduced for the prediction of d...
The pharmaceutical industry is facing unprecedented pressure to increase its productivity. Attrition...
As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients...
Whereas drugs are intended to be selective, at least some bind to several physiologic targets, expla...
In this dissertation, I present several strategies to leverage experimental data towards a quantitat...
Drug research and development is the driving force of the pharmaceutical industry, and it has also c...
Two problems now threaten the future of anticancer drug development: (i) the information explosion h...
The role of computational tools in the drug discovery and development process is becoming central, t...
An expanded chemical space is essential for improved identification of small molecules for emerging ...
Prediction of novel drug indications using network driven biological data prioritization and integra...