Receptor AI - Accelerated Drug Discovery

Targeting cryptic pockets with Receptor.AI drug discovery platform Every idea needs a Medium

AI-based prediction of small molecules’ selectivity for highly similar protein variants Every idea needs a Medium

The key for fast and efficient drug discovery: benchmarking the Receptor.AI ADMET prediction model Every idea needs a Medium

The rock-solid base for Receptor.AI platform: benchmarking the drug-target interaction AI model Every idea needs a Medium

Identification of dual-targeting Mycobacterium tuberculosis aminoacyl-tRNA synthetase inhibitors… Every idea needs a Medium

Developing bifunctional protein degraders for difficult-to-drug proteins with the Receptor.AI Every idea needs a Medium

Receptor.AI introduces 3DProtDTA — the next generation drug-target interaction AI model Model description and performance benchmarks.

Receptor.AI’s end-to-end drug discovery platform avoids computational overkill and embraces… The companies, which provide drug discovery services, deal with two different types of customer requests. One of them is a request to help…

The lipid membranes are druggable and available for AI-based drug design Are the lipid membranes druggable?

Receptor.AI helps fight obesity by acting on the “sixth taste” receptors Obesity is one of the health problems which seems to be minor in comparison to cancer or neurodegenerative disorders from an individual…

DNA-Encoded Libraries (DELs) are one of the most powerful technologies in drug discovery. However, its combination with AI techniques could be even more successful. Here we describe how these two disruptive techs could work together.

https://medium.com/receptor-ai/ai-meets-del-is-this-the-most-powerful-combo-in-modern-drug-discovery-c78eb3ad6320

AI meets DEL: Is this the most powerful combo in modern drug discovery? The power of AI may bring DEL screening to the next level

RECEPTOR.AI aims to democratize drug discovery, making its AI platform accessible to SMEs and academia. To demonstrate our platform's strength and user-friendly functionality, we are offering 5 organizations the ability to use 40+ AI models integrated into the SaaS platform for novel hit discovery for free.
What's included:

- AI-based virtual screening of the 4.5B Enamine’s REAL® database against one out of ~10K protein targets integrated into our system (https://docs.google.com/spreadsheets/d/1_MCl8x4AJDsvMcUkWfb0Lvvn7K6YJYx9/edit?usp=sharing&ouid=107056446530886884736&rtpof=true&sd=true);

- Ability to select any number among 38 ADME-Tox predictive models for filtering the unsafe and ineffective compounds (https://drive.google.com/file/d/1DRT0NPDfqrmzXYdbMLI6cqV3rlK0rrmq/view?usp=sharing);

- Off-target interactions and selectivity assessment against ~10K targets;

- Automated docking of top compounds with AI-based rescoring;

- Technical support and training.

How to participate:

- Complete the application form: https://docs.google.com/forms/d/1Q_pIe9huHpkQs_jlG2sSJ34XTtr36qIVlPSQ9xnSFAY/edit;

- We will select one company for 4.5B Enamine’s REAL® database screening and four organizations for 3.5M Enamine stock compounds database screening on the 8th of October 2022. The winners will be announced on our Linkedin page and notified by email;

- Eligible companies must be working in the drug discovery field, have up to 150 employees, and have been founded in the last 5 years;

- Academic institutions are free to participate and only limited by the focus on small molecule drug discovery.

For more information on the solutions Receptor.AI offers please visit: https://receptor.ai/materials

We are proud to share a comparative study of Receptor.AI virtual screening platform against 24 competing techniques, which shows an excellent performance of our technology that is on par or superior to all of its rivals.

https://medium.com/receptor-ai/the-proof-of-excellence-comparative-study-of-receptor-ai-virtual-screening-technologies-9fb524839e4c

The Proof of Excellence: comparative study of Receptor.AI virtual screening technologies Comparative analysis with other techniques for small molecule drug discovery

RECEPTOR.AI SaaS Platform for AI-based drug discovery was validated on several clinically important protein targets. One of them is BRD4, which is used to fight leukaemia. Our platform allowed obtaining several hit compounds from the previously unknown class of BRD4 inhibitors with promising ADMET parameters.
https://medium.com/receptor-ai/developing-novel-inhibitors-of-brd4-to-fight-leukaemia-with-receptor-ai-saas-platform-fa2f4bb3d123

Developing novel inhibitors of BRD4 to fight leukaemia with RECEPTOR.AI SaaS Platform Case study of the platform usage

The success of AI-based drug discovery crucially depends on the quality of data used for model training. However this comes with significant added cost of of labor required to build, deploy, maintain and manage the AI models and the underlying data. Receptor.AI drug discovery platform is built from the ground up to automate the AI-based drug discovery workflow and to minimize human intervention to all routine data-related processes. This allows all members of R&D team to concentrate on productive work towards strategic goals.

https://medium.com/receptor-ai/data-rules-it-all-in-ai-based-drug-discovery-a5d4c3b9c439

Data rules it all in AI-based drug discovery Automation of data-related tasks is crucial for the success of big pharma and SME biotech in drug discovery

There is a certain disappointment in AI drug discovery among early adopters of this technology. The users have quickly realised that AI is not a silver bullet which will magically eliminate all the complexity and cost of the drug discovery process. However, the confidence in AI solutions could be restored by providing the users correct tools, which answer the correct questions.

https://medium.com/receptor-ai/can-automated-ai-drug-discovery-platforms-increase-the-confidence-of-pharma-in-ai-techniques-a75bef27a63c

Can automated AI drug discovery platforms increase the confidence of pharma in AI techniques? Is it possible to overcome the frustration of early adopters of AI drug discovery techniques?

RECEPTOR.AI provides the tools for building effective and customizable AI workflows to approach drug discovery projects of any complexity. Our next-generation platform for drug discovery is not only AI-powered but also AI-automated and AI-assisted.

More information: https://receptor.ai/materials/

The Receptor.AI makes automated AI solutions for drug discovery accessible to big pharma, medium biotech and academic institutions alike. The Receptor.AI platform automates the major stages of AI-based workflow, namely data preparation, model training and pipeline construction for drug discovery in highly customizable and user friendly manner.

https://medium.com/receptor-ai/receptor-ai-democratizes-automated-ai-solutions-for-drug-discovery-e7eb937cb305

Receptor.AI “democratizes” automated AI solutions for drug discovery The Receptor.AI platform automates the major stages of AI-based drug discovery workflow

Detailed information about our Our AI-accelerated SaaS platform is now available with a demo video of its interface and capabilities. We are ready to provide it for our customers at very competitive prices and with a flexible subscription model.

https://medium.com/receptor-ai/public-launch-of-the-receptor-ai-saas-platform-for-drug-discovery-d0d3441a65c8

Public launch of the Receptor.AI SaaS platform for drug discovery AI-accelerated automated drug discovery at very competitive prices

Receptor.AI announces the latest version of our AI-accelerated SaaS platform to disrupt drug discovery.

If you are an SME interested in using the latest AI techniques, you will be pleased to benefit from the virtual screening of multi-billion chemical spaces within an hour. Moreover, our platform is integrated with two major synthesis manufacturers to provide design-make-test solutions in flexible pricing plans.

Keep in touch, so we will be happy to demonstrate our platform benefits.

According to recent estimates, up to 85% of all human proteins are considered undruggable.

Different classes of undruggable targets require different approaches and there is no guarantee that any undruggable target could be made druggable. Nevertheless, even a small fraction of new druggable targets may lead to a huge progress in pharmacology and medicine, and result in cures for untreatable diseases.

Receptor.AI is joining other companies in chasing the Holy Grail of undruggable targets using its unique technology stack and scientific expertise.

https://medium.com/receptor-ai/undruggable-targets-the-major-challenge-for-modern-drug-discovery-52a87ccb94f8

Undruggable targets: the major challenge for modern drug discovery The AI approaches for beating undruggability

The CLC channels belong to a large family of ionic channels which translocate chloride ions across the cell membranes.

Receptor.AI announced a new approach to finding selective CLC-K, based on complementary usage of several AI-based techniques and in silico modeling.

Read more in our latest press release: (https://lnkd.in/drVsRnth)

It is a well-known fact that most of the field of AI-based drug development is oriented towards big pharma. Although very convenient and profitable, this method largely overlooks the innovative potential of small and medium pharmaceutical companies.

In 2020, 133 new drugs were approved by EU and US authorities, but only 37% of them were developed by pharmaceutical giants, while 63% originate from small and medium enterprises.

Receptor.AI is a new player in the drug discovery space, aiming at changing the situation by «democratizing» access to cutting-edge AI technologies for smaller pharma and biotech companies.

Read the whole story here (https://www.biopharmatrend.com/post/466-receptorai-contributes-to-the-future-of-ai-based-drug-discovery-by-diversifying-its-user-base/).

It is clear that the Alzheimer's disease (AD) is so complex that traditional approaches fail not only in its treatment but also in understanding of its biological basis and diagnostics.

As usual, Machine Learning techniques may help to make sense out of the flood of heterogeneous data. The recent review identify several major areas where ML could be beneficial in AD research.

Learn more in RECEPTOR.AI's new blog post (https://medium.com/receptor-ai/is-alzheimers-disease-too-complex-even-for-ai-d3eb21d2354d) (reading time: 5 mins).

Food poisoning is a common condition, experienced by almost all people at least once in their lives.

The whole-genome sequencing (WGS), which is now relatively cheap and accessible, comes to the rescue, allowing to trace the reservoir of bacteria. In practice, this method is hampered by overwhelming amounts of information.
As usually in such cases, the Machine Learning techniques allow to tame the chaos in the data.

Learn more in RECEPTOR.AI's new blog post (https://medium.com/receptor-ai/ai-could-be-used-to-reduce-bacterial-food-poisoning-f0c76fa26970) (reading time: 3 mins).

Happy New Year to all our network!
This is the first New Year for Receptor.AI and we are excited about what we managed to achieve in this time.

- We have developed over 40 proprietary AI models for drug discovery and established our in-house drug discovery platform.
- We have successfully finished three pilot projects and signed Letters of Agreement (LOI) with large chemical providers.
- We have successfully tested a unique MVP for screening of the ultra-large chemical databases (>20B molecules) in one day.
- We have launched 7 research collaborations on neurodegenerative, cardiovascular, oncological diseases, and obesity with our partners from the USA, France, India, Germany, and Ukraine.
- We have published our first scientific paper and are currently working on another three.
- We have registered in the European Commission registry of research partners and applied for three large collaborative research grants.
- We have become an Amazon Web Services (AWS) partner and are working toward official certification.
- We are establishing our first connections with big pharmaceutical companies.

We are optimistic and enthusiastic about the year to come! Our team will continue to work on the best-in-class AI solutions for drug discovery immediately after a short New Year break. Happy Holidays and Happy New Year!

and Happy Holidays to our entire network of family and friends!

Polypharmacology refers to a single drug that acts on multiple targets, which may be associated with a single disease pathway or several different disease pathways.

Learn how AI contributes to understanding polypharmacology with explainable machine learning in a new article (https://medium.com/receptor-ai/ai-contributes-to-understanding-polypharmacology-with-explainable-ml-3ab726d2c963) by RECEPTOR.AI's Chief Scientific Officer Semen Yesylevskyy (reading time: 4 mins).

The problem of chemical synthesis in drug discovery is often considered “solved” and purely technical.

It is based on an overwhelming amount of data; thus, it is tempting to deal with it using machine learning.

Learn more here https://lnkd.in/e6PUQmv4 (reading time: 7 mins).

Happy Birthday to CEO of RECEPTOR.AI Alan Nafiiev! 🎉🎊