machine learning

As clinical trials have become more decentralized, there has been an increased focus on the need for more patient-centric drug development. This focus has led to a variety of eClinical applications. Electronic patient-reported outcomes (ePRO) and other electronic clinical outcome assessment (eCOA) approaches can transform trials to make them more pragmatic, patient-centric and efficient. Such…

MIT researchers are touting a new machine-learning technique called DeepBAR that can quickly calculate the binding affinities between drug candidates and their targets. DeepBAR produces precise calculations in a fraction of the time compared to conventional techniques, according to the researchers. They think the software could potentially accelerate drug discovery and protein engineering. “Our method…

The concept of precision drug-dosing has gained ground in recent years, given its ability to boost efficacy and curb side effects. Yet imprecise dosing regimens continue to be common for many drugs, leading to significant rates of adverse drug reactions (ADRs).  “ADRs are one of the top ten causes of death in the developed world,”…

For all of its promise in healthcare and elsewhere, deploying artificial intelligence is frequently a challenging endeavor. “Close collaboration between data science teams, other project team members and stakeholders is essential,” said Jennifer Bradford, director of data science at Phastar, the London-headquartered contract research organization. While input from computational, statistical or medical experts could be…

Researchers at Purdue University have devised chemical reactivity flowcharts and novel machine learning models that could accelerate drug discovery. Although the use of machine learning for drug development has grown, researchers’ ability to understand machine learning recommendations has been limited. In a paper published in Organic Letters, Purdue professor Gaurav Chopra noted that the machine…