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Data Integration in Drug-Target Interaction Prediction for Drug Discovery and Drug Repositioning KU Leuven
Approved drugs have favorite or validated pharmacokinetic properties and toxicological profiles, and the repositioning of existing drugs for new indications can potentially avoid expensive costs associated with early-stage testing of the hit compounds. In recent years technological advances in virtual screening methodologies have allowed medicinal chemists to rapidly screen drug library for therapeutic activity against new biomolecular targets. ...
Estimation of an in vivo fitness landscape experienced by HIV-1 under drug selective pressure useful for prediction of drug resistance evolution during treatment KU Leuven
MOTIVATION: HIV-1 antiviral resistance is a major cause of antiviral treatment failure. The in vivo fitness landscape experienced by the virus in presence of treatment could in principle be used to determine both the susceptibility of the virus to the treatment and the genetic barrier to resistance. We propose a method to estimate this fitness landscape from cross-sectional clinical genetic sequence data of different subtypes, by reverse ...
Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells Universiteit Gent
In precision oncology, therapy stratification is done based on the patients’ tumor molecular profile. Modeling and prediction of the drug response for a given tumor molecular type will further improve therapeutic decision-making for cancer patients. Indeed, deep learning methods hold great potential for drug sensitivity prediction, but a major problem is that these models are black box algorithms and do not clarify the mechanisms of action. This ...
Scaling machine learning for target prediction in drug discovery using Apache Spark Vrije Universiteit Brussel KU Leuven
In the context of drug discovery, a key problem is the identification of candidate molecules that affect proteins associated with diseases. Inside Janssen Pharmaceutica, the Chemogenomics project aims to derive new candidates from existing experiments through a set of machine learning predictor programs, written in single-node C++. These programs take a long time to run and are inherently parallel, but do not use multiple nodes. We show how we ...
Rethinking protein drug design with highly accurate structure prediction of anti-CRISPR proteins Universiteit Gent
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen Universiteit Gent Vrije Universiteit Brussel Universiteit Hasselt KU Leuven
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell ...
Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction KU Leuven
BACKGROUND: Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data. OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated ...
A community effort to assess and improve drug sensitivity prediction algorithms Universiteit Gent
Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug ...
Local anomalous drug diffusion at healthy-cancer tissue surface and data-driven tumor growth model prediction Universiteit Antwerpen Universiteit Gent
This paper discusses reliable yet minimal computational models for predicting the patient's response to anticancer multi-drug combined therapy. The distribution of the drugs into the local heterogeneity of healthy-tumor tissues can be translated into mathematical models. Ideally, these should best describe the physiological processes and physical mechanisms, together with the interactions between the contributing components of the tumor growth ...