AI Drug Development Startups
From discovery, preclinical and clinical phase, to drug researching mechanisms and drug repurposing during drug development of natural compounds (i.e. psychedelics), using AI
This blog is dedicated to AI startups during drug development, from discovery, preclinical and clinical phase, to drug researching mechanisms and drug repurposing during drug development of natural compounds (i.e. psychedelics) using AI.
Drug development starts always with screening different molecules and testing them on hundreds of different types of human cells (200 different types of human cells) expressing hundreds of different types of receptors and other proteins, not to mention that every human is different and human cells still evolve.
Considerations on the number of possible molecules to screen has led to the concept of the “chemical space” (known and unknown) to describe the ensemble of all organic molecules to be considered when searching for new drugs.
Whereas the known chemical space including public databases and corporate collections probably contains 100 million molecules, it has been estimated that the unknown virtual chemical space might contain as many as 10⁶⁰ (novemdecillion) compounds when considering only basic structural rules, or a more modest 10²⁰–10²⁴ molecules if combination of known fragments are considered. In comparison, the number of sand grains on Earth is about 7.5 x 10¹⁸. These estimations suggest that this entire chemical space is far too large for an exhaustive enumeration, even using today’s computers. One is therefore left with a partial, targeted enumeration as the only option to produce molecules for virtual screening.
In particular, this review of 2020 “Review on natural products databases: where to find data in 2020” is a complete overview of various databases and collections of natural products (NP) that can be used during drug discovery (over 120 different NP databases and collections published and re-used since 2000 and 98 of them are still somehow accessible and only 50 are open access).
Keep in mind that for every 10000 compounds screened in discovery, approximately 250 compounds will pass through to more rigorous preclinical studies. Eventually, 5 compounds of these 250 will pass to highly regulated clinical trials (Phase1–3). Excluding cancer drugs from the results — which made up fully 31% of all drug programs studied and have overall success rates 5.1% —the overall success rate of all other drugs that enter Phase 1 clinical trials and ultimately reaching FDA approval is 11,9%. The entire process of drug development takes 10 to 15 years and $2.6 billion to bring a drug to market.
Therefore, in order to make this process more efficient hundreds of new data driven startups are working right now from discovery and preclinical phase to clinical phase, trying to optimise every single step of drug development with AI and ML tools.
Let’s see now these startups:
Drug discovery phase (screening, chemical synthesis and drug design) startups🔬
AI preclinical phase startups🐭
AI startups for data aggregation process of the entire AI drug development process 📑📊📈
AI clinical trials startups (design, recruitment and optimisation of clinical trials and digital health: digital therapeutics, wearables and telemedicine)💉
AI Drug repurposing Startups 🌀
AI Drug Researching mechanism startups 🕵🏻
Natural Compounds (i.e. Psychedelics) Drug Development Startups 🍄
Psychedelic Drug Development (Best 2022)
AI dissemination of biomedical science
AI and Health
Thank you for reading 💙
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