Computational techniques have been applied in the drug discovery pipeline since the
1980s. Given the low computational resources of the time, the first molecular modeling …

Interests in the impact of the gastrointestinal microbiota on health and wellbeing have
extended from humans to that of companion animals. While relatively fewer studies to date …

Structure-based virtual screening relies on classical scoring functions that often fail to
reliably discriminate binders from nonbinders. In this work, we present a high-throughput …

The methodology development and applications of ReaxFF molecular dynamics (ReaxFF
MD) in unraveling the complex reactions and kinetics for pyrolysis and oxidation of organic …

Current genomics methods are designed to handle tens to thousands of samples but will
need to scale to millions to match the pace of data and hypothesis generation in biomedical …

DNA sequencing was dominated by Sanger's chain termination method until the mid-2000s,
when it was progressively supplanted by new sequencing technologies that can generate …

Optimization of electrostatic complementarity is an important strategy in structure-based drug
discovery for improving the affinity of molecules against a specific protein target. In this …

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite
playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this …

Graphics Processing Units (GPUs) exploit large amounts of threadlevel parallelism to
provide high instruction throughput and to efficiently hide long-latency stalls. The resulting …

High-throughput experiments enable researchers to explore complex multifactorial diseases
through large-scale analysis of omics data. Challenges for such high-dimensional data sets …