Closing the data loop in AI-driven drug discovery
Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today,
Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today, bringing a new drug to market takes an average of 10-15 years and costs anywhere from $1 billion to $2.
5 billion, with failure rates upward of 90%. AI has become the pharmaceutical industry’s biggest bet on bringing success rates up and timelines down. The faster drug companies can identify, test, and optimize new chemical compounds, the lower the risk of costly failures later in development.
“The main cost in drug discovery is still the clinical phase, so trying to reduce risk and increase your success rates there is obviously hugely beneficial,” says Paul Belcher, director of protein research strategy at global life sciences company Cytiva. “AI is one approach that drug companies hope will not only save time and compress timelines, but enable better quality candidates to reach the clinic.” Early use of AI in drug discovery shows potential, but also highlights the need for robust and authentic data, as well as integration in lab systems.
AI brings efficiency to the lab One of the most promising early-stage applications of AI in drug discovery is in hit identification. This involves screening libraries of molecular entities against a disease-related target, such as a protein, to find molecules that bind to it. A successful hit gives researchers a starting point for further testing and refinement, with the aim of eventually developing a viable drug.
Belcher has seen a shift from empirical screening to predictive design: Instead of physically screening libraries, drug companies are now using AI to design drug candidates from scratch and predict how they will interact with disease targets before committing anything to research and development (R&D). This means companies are no longer limited b
Đọc thêm từ Công nghệ
Bessemer, Atomico in talks to lead The Exploration Company funding at $2bn valuation
Franco-German scaleup The Exploration Company, which makes reusable space capsules, is in advanced talks for a new round set to be co-led by Atomico and Bessemer Venture Partners which will value the scaleup at around $2bn post-money.Csw mwnkszl ms yfh ds xobeo gvkxid $190a, azc
Way Security, which uses AI automation and agentic workflows to help deploy IAM systems, raised a $20M seed from Insight Partners and Glilot Capital (Chris Metinko/Axios)
Chris Metinko / Axios: Way Security, which uses AI automation and agentic workflows to help deploy IAM systems, raised a $20M seed from Insight Partners and Glilot Capital — Way Security raised a $20 million seed round from Insight Partners and Glilot Capital, CEO Yossi Barishev
The Hidden Cost Of AI Coding: Why Context Matters More Than Tokens
AI has created a growing disconnect between activity and outcomes. More code is generated, but not necessarily more value.
Beyond the hyperscale cloud: DFINITY's vision for sovereign computing
As governments race to define digital sovereignty and enterprises reconsider their dependence on hyperscale cloud providers, most of the debate remains theoretical. Dominique Williams, founder and Chi...