Marina T Alamanou
Marina T Alamanou
Life Science Consultant
Life Science Content Creation
Drug Design: AI tools and startups
In my previous articles I reviewed 🖊 some of the different AI tools and startups for 🔹 primary and secondary screening for the identification of drug candidates (bioactivity and toxicity prediction and physicochemical properties) and for 🔹planning and execution of chemical synthesis during drug discovery. Consequently, today I am going to talk about AI tools and startups for drug design during drug discovery, namely target-protein structure prediction and drug-protein interactions.
Planning and execution of chemical synthesis: tools and startups
Drug discovery is the process by which new drugs are discovered and involves: 🔹the identification of screening hits (high throughput screening in vitro and secondary assays), 🔹medicinal chemistry (design and synthesis) and 🔹optimisation of those hits to increase the affinity, selectivity, efficacy, metabolic stability and oral bioavailability. Plus in silico studies in combination with cellular functional tests are used to improve the functional properties of the drug candidates. Once a candidate compound that fulfills all of these requirements has been identified, the process of drug development can continue with preclinical and clinical trials.
AI Pharma Deals: Roche (Genentech) and AI Startups
Roche (Genentech) data-centric drug discovery According to a report by Grand View Research, Inc. the global AI in drug discovery market size (240 AI startups, 600 investors, 90 corporations) is expected to reach USD 3.5 billion💵 by 2027, expanding at a CAGR of 28.8%. Right now, half of the world’s AI drug discovery startups have their headquarters in US, follows UK, EU, Canada and Asia currently has the fifth-lowest proportion of AI for drug discovery startups.
Primary and secondary screening: tools and startups
Drug discovery is how new medications are discovered and involves: screening hits (high throughput screening in vitro and secondary assays), design and synthesis (medicinal chemistry), optimisation of hits to reduce potential drug side effects (increasing affinity and selectivity), and in silico studies that in combination with cellular functional tests are used to improve the functional properties of the drug candidates.
AI Pharma Deals: GSK and AI Startups
GlaxoSmithKline (GSK) and AI/ML data-driven drug discovery 🤖 Over the last seven years, there has been an increase in data digitalisation in the pharmaceutical sector and that motivates the use of AI, that can handle large volumes of data with enhanced automation.
AI Pharma Deals: Takeda and AI Startups
Takeda 💊 AI Partnerships 🤖 A GlobalData analysis showed that the number of pharma AI partnerships has risen significantly since 2015 and in particular in 2020 27 partnerships were identified in this field, that is an increase of 575% in the last six years (link).
AI Pharma Deals: AstraZeneca and AI Startups
AI/ML Drug Discovery and AstraZeneca Right now, it takes on average more that 10 years (sometimes nearly 15 years) to bring a new drug to the market with the cost of developing a new prescription drug that gains market approval being approximately $2.6 billion💵, while an astonishing proportion of this money is lost in the 90% of drug candidates that fail during the drug development process.
AI Pharma Deals: Pfizer and AI Startups
When the pharma giant Pfizer met AI AI in Pharma refers to the use of algorithms to perform tasks which traditionally rely on human intelligence. Currently, AI is being applied in pharma for Diagnosis, Drug Discovery, Clinical Trials, Manufacturing and Predictive Medicine (and of course Supply Chain, Sales, and Customer Service).