How far away is fully computational drug discovery?

Jun 10, 2020 | Blog | 0 comments

Daniel Gigante

Our CEO was recently asked how far away is fully computational drug discovery and development. This isn’t an easy question to answer and Andrew doesn’t provide an exact date. However, he does provide interesting insight into the progress we’ve made and the challenges ahead. Give his response a listen below.

As Andrew explains, the first step is proving that computational drug discovery works. Currently, there are a drugs in clinical trials, but none on the market. Additionally, there’s the question of how we substitute in vitro and vivo tests for in silico tests. However, the biggest hurdle we’ll likely face, in a push towards fully computational drug discovery, is industry skepticism.

0 Comments

Submit a Comment

Your email address will not be published. Required fields are marked *

EVQLV and Manhattan BioSolutions Enter into Strategic Collaboration to Discover Novel Antibody-Based Therapies

The collaboration fuses EVQLV’s expertise in AI-enabled antibody design & Manhattan BioSolutions’ expertise in antibody development in oncology

Demystifying AI In Pharma

Examining AI as automated, assistive, analytical, accelerated, or augmented intelligence is a framework for thinking about how to apply these tools to data in ways that are aligned with an organization’s goals. This framework is designed to allow those who do not routinely build or employ AI to avoid the overhyped rhetoric of AI and consider the practical outcomes of employing it.

Andrew Satz on the Amplifying Scientific Innovation Podcast with Dr. Sophia Ononye-Onyia

EVQLV CEO, Andrew Satz, recently joined the Amplifying Scientific Innovation podcast with Dr. Sophia Ononye-Onyia. They discussed his definition of scientific innovation, personal anecdotes of his CEO journey and EVQLV’s unique approach to scientific innovation through the acceleration of biologic therapies with the power of artificial intelligence (A.I.).

How Our Algorithm Models the Drug Discovery Process

Our CEO, Andrew Satz, recently spoked at “Alumni in Conversation,” hosted by Columbia University School of General Studies. During the discussion, Andrew explained how EVQLV’s algorithm models the drug discovery process.

Large Pharma’s Embrace of Artificial Intelligence

The benefits of computational drug development are well known and accepted by many in the pharmaceutical space. However, since the onset of the global pandemic, consensus went from it being an interesting concept that was almost certainly the future, to something we need implemented as soon as possible. This is one of the few silver linings of the coronavirus outbreak.

Pin It on Pinterest