AIMC Topic: Drug Discovery

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Deep Learning: Current and Emerging Applications in Medicine and Technology.

IEEE journal of biomedical and health informatics
Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical ...

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery.

Chemical biology & drug design
In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, includin...

Exploring Tunable Hyperparameters for Deep Neural Networks with Industrial ADME Data Sets.

Journal of chemical information and modeling
Deep learning has drawn significant attention in different areas including drug discovery. It has been proposed that it could outperform other machine learning algorithms, especially with big data sets. In the field of pharmaceutical industry, machin...

PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks.

Journal of chemical information and modeling
Drug discovery suffers from high attrition because compounds initially deemed as promising can later show ineffectiveness or toxicity resulting from a poor understanding of their activity profile. In this work, we describe a deep self-normalizing neu...

eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates.

BMC pharmacology & toxicology
BACKGROUND: The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques an...

Clinical intelligence: New machine learning techniques for predicting clinical drug response.

Computers in biology and medicine
Predicting the response, or sensitivity, of a clinical drug to a specific cancer type is an important research problem. By predicting the clinical drug response correctly, clinicians are able to understand patient-to-patient differences in drug sensi...

Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning.

Journal of chemical information and modeling
Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and subst...

Prediction of matrix metal proteinases-12 inhibitors by machine learning approaches.

Journal of biomolecular structure & dynamics
Matrix metal proteinases-12 (MMP-12) is a hot pharmaceutical target on the treatment of many human diseases. There's a crying need for designing and finding new MMP-12 inhibitors. In this work, four machine learning approaches, support vector machine...

Disease comorbidity-guided drug repositioning: a case study in schizophrenia.

AMIA ... Annual Symposium proceedings. AMIA Symposium
UNLABELLED: The key to any computational drug repositioning is the availability of relevant data in machine-understandable format. While large amount of genetic, genomic and chemical data are publicly available, large-scale higher-level disease and d...