A New Player In Drug Discovery Promises to "Democratize" AI Solutions, Brings Flexibility

Artificial Intelligence (AI) in drug discovery is now on a steep rise. A growing number of companies compete to develop new drugs faster, cheaper, and with a much higher success rate using AI tools at all crucial stages of the drug discovery pipeline.

Most of the players in this quickly expanding market are oriented towards big pharma, which is routinely investing billions into drug development. Such a partnership is tempting not only for startup companies but also for established leaders in the field of AI-based drug development, because it provides stable multi-year contracts backed up by financial resources and infrastructure of the pharmaceutical giants.

As a result, typical AI-based drug discovery services are tailored for these kind of large corporate customers. For example, confidentiality and security issues push big pharma to carry out the on-premise installation of the software with long-term on-site maintenance contracts. The need for integration into the complicated internal workflow of a large company reduces versatility and leads to interface bloat.

Somewhat paranoid fear of data leaks substantially limits the potential of cloud scaling and requires the usage of complex access rights management and advanced encryption schemes. All this makes such solutions much more expensive than their straightforward SaaS equivalents. The excessive price and complexity drive them out of reach for medium biotech and academic science.

Although orientation on big pharma is very convenient and profitable for AI-based drug discovery companies, it 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, often in collaboration with academic research institutions. The usage of AI drug discovery technologies in this sector is still insufficient and there is a strong demand for solutions, which are friendly to small companies and academic scientists.