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 …

Ligand-and structure-based drug design approaches complement phenotypic and target
screens, respectively, and are the two major frameworks for guiding early-stage drug …

Background In past, numerous quantitative structure-activity relationship (QSAR) based
models have been developed for predicting anticancer activity for a specific class of …

Background Recently, novel 3D alignment-free molecular descriptors (also known as
QuBiLS-MIDAS) based on two-linear, three-linear and four-linear algebraic forms have been …

The prediction of binding poses and affinities is an area of active interest in computer-aided
drug design (CADD). Given the documented limitations with either ligand or structure based …

Novel N-tuple topological/geometric cutoffs to consider specific inter-atomic relations in the
QuBiLS-MIDAS framework are introduced in this manuscript. These molecular cutoffs permit …

Cheminformatics utilizes various computational techniques to solve a wide variety of drug
discovery problems, including drug design and predictive toxicology. These computational …

The D3R 2015 grand drug design challenge provided a set of blinded challenges for
evaluating the applicability of our protocols for pose and affinity prediction. In the present …

Abstract This paper investigates Recurrent Neural Networks (RNNs) in the context of virtual
High-Throughput Screening (vHTS). In the proposed approach, RNNs, particularly …

Drug combination therapies have numerous advantages in contrast to mono therapies alone
and are commonly used for cancer treatment. If the right drugs are combined it is possible to …