Molecular fingerprint similarity search in virtual screening
Molecular fingerprints have been used for a long time now in drug discovery and virtual
screening. Their ease of use (requiring little to no configuration) and the speed at which …
screening. Their ease of use (requiring little to no configuration) and the speed at which …
Data-driven algorithms for inverse design of polymers
The ever-increasing demand for novel polymers with superior properties requires a deeper
understanding and exploration of the chemical space. Recently, data-driven approaches to …
understanding and exploration of the chemical space. Recently, data-driven approaches to …
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
Motivation While drug combination therapies are a well-established concept in cancer
treatment, identifying novel synergistic combinations is challenging due to the size of …
treatment, identifying novel synergistic combinations is challenging due to the size of …
The Chemistry Development Kit (CDK) v2. 0: atom typing, depiction, molecular formulas, and substructure searching
Abstract Background The Chemistry Development Kit (CDK) is a widely used open source
cheminformatics toolkit, providing data structures to represent chemical concepts along with …
cheminformatics toolkit, providing data structures to represent chemical concepts along with …
DeepTox: toxicity prediction using deep learning
The Tox21 Data Challenge has been the largest effort of the scientific community to compare
computational methods for toxicity prediction. This challenge comprised 12,000 …
computational methods for toxicity prediction. This challenge comprised 12,000 …
ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation
Background Molecular descriptors and fingerprints have been routinely used in QSAR/SAR
analysis, virtual drug screening, compound search/ranking, drug ADME/T prediction and …
analysis, virtual drug screening, compound search/ranking, drug ADME/T prediction and …
SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction
Synergistic drug combinations can improve the therapeutic effect and reduce the drug
dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen …
dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen …
[HTML][HTML] iPSC-based compound screening and in vitro trials identify a synergistic anti-amyloid β combination for Alzheimer's disease
T Kondo, K Imamura, M Funayama, K Tsukita… - Cell reports, 2017 - cell.com
In the process of drug development, in vitro studies do not always adequately predict human-
specific drug responsiveness in clinical trials. Here, we applied the advantage of human …
specific drug responsiveness in clinical trials. Here, we applied the advantage of human …
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Deep learning is currently the most successful machine learning technique in a wide range
of application areas and has recently been applied successfully in drug discovery research …
of application areas and has recently been applied successfully in drug discovery research …
ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics
Chemogenomics data generally refers to the activity data of chemical compounds on an
array of protein targets and represents an important source of information for building in …
array of protein targets and represents an important source of information for building in …