According to Insilico Medicine, a Maryland-based company developing artificial intelligence (AI) for drug discovery and biomarker development, partnerships between artificial intelligence (AI) companies and biotechs is often impeded by contractual issues.
To develop a more flexible partnering process, Insilico Medicine recently contracted Hill Dickinson, an international commercial law firm based in the UK, to create a standardized documentation system.
According to Alex Zhavoronkov, CEO of Insilico Medicine, the universal partnership agreement is intended to simplify and standardize the partnership process. Standardized documentation will be integrated into the automated partnering system already in use by Insilico and will be developed using machine learning.
“To our knowledge, it is the first time in AI for drug discovery that someone developed a universal flexible partnering agreement that describes the multiple partnering options and provides the ability to structure the agreement in a semiautomatic manner,” he told us.
Michael Corcoran, a partner at Hill Dickinson, explained that the system can be modified for other clients as well. “We believe this will be a big growth in the area for the firm over the next 12-24 months,” he told us.
As Corcoran explained, a simple and repeatable contract needing minimum tailoring can accelerate the process from three months to three weeks. This time reduction, he said, is especially important to suppliers working on multiple projects that often have fees based on royalties and milestones.
For Insilico, the company is working with smaller biopharma companies, which Zhavoronkov said that
want to leapfrog the big pharma R&D processes.
“They partner quickly and can provide access to experimental data. This can significantly accelerate the drug discovery process: Using AI the teams innovate faster or fail quicker, significantly reducing the burden of traditional R&D,” he told us.
Insilico partnered with Juvenescence as part of a multi-year drug development agreement to advance compounds discovered using AI.
As of today, Zhavoronkov saidits AI pipeline allows the company to identify targets and generate novel molecules for them, but it is planning to validate its pipeline even more to establish molecules for several disease areas.
Zhavoronkov added, “It is now clear that the best way to keep a sustainable business as an AI company is to develop assets that biotechnology and pharma companies can acquire. We are building such a pipeline.”