In 2016, the FAIR Guiding Principles for scientific data management and stewardship were published, laying out guideposts for scholarly data producers to make their data discoverable and usable in the future. The FAIR principles seek to ensure that data is Findable, Accessible, Interoperable, and Reusable. At the time of their publication, they articulated and centralized many points…
Digital transformation will give scientists their time back and speed the development pipeline
The pandemic exposed the innovation divide between the digitally transformed and those that lagged. Strict regulation made life sciences and bio/pharma organizations hesitant to modernize too quickly away from proven legacy methods and technologies, resulting in varying levels of digital transformation. But since the pandemic, organizations now recognize the necessity of digitalization and smart automation…
Cutting through the noise of machine learning for drug discovery
While the topic of AI in drug discovery has received considerable attention in recent years, mature deployments of techniques such as machine learning in the industry remain rare. “The chemistry domain is qualitatively different from any other problem that machine learning has exhibited real success in,” said Jason Rolfe, CTO of Variational AI (Vancouver, Canada). …
On ePRO and ensuring data integrity
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 tout new machine learning technique for assessing drug molecules
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…
How precision drug-dosing supports individualized treatment
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,”…
Q&A: Keys to unlock data science potential for drug discovery
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 aim to speed drug discovery with human-understandable ML models
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…