Journal of chemical information and modeling
Jan 24, 2019
Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excr...
IEEE journal of biomedical and health informatics
Jan 23, 2019
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 ...
International journal of molecular sciences
Jan 17, 2019
The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be...
PURPOSE: Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse side effect of cancer chemotherapy that can be life debilitating and cause extreme pain. The multifactorial and poorly understood mechanisms of toxicity have impeded the i...
Journal of chemical information and modeling
Dec 21, 2018
A key component of automated molecular design is the generation of compound ideas for subsequent filtering and assessment. Recently deep learning approaches have been explored as alternatives to traditional de novo molecular design techniques. Deep l...
Journal of chemical information and modeling
Dec 18, 2018
We present DrugScore, a new version of the knowledge-based scoring function DrugScore, which builds upon the same formalism used to derive DrugScore but exploits a training data set of nearly 40 000 X-ray complex structures, a highly diverse and the,...
We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables th...
Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learni...
: Artificial intelligence systems based on neural networks (NNs) find rules for drug discovery according to training molecules, but first, the molecules need to be represented in certain ways. Molecular descriptors and fingerprints have been used as ...