Iktos Announces Collaboration With Pfizer in AI for Drug Design
“We are proud to work with Pfizer and to have their scientists use our software in their early discovery programs”
Iktos announced the application of Iktos artificial intelligence technology for de novo design to selected Pfizer small-molecule discovery programs.
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Over the last few years, phenomenal advances in AI algorithmic development and computational power have enabled innovative approaches in small-molecule drug design. Iktos has been at the forefront of these efforts putting its generative modeling technology at work in several collaborations with pharmaceutical and biotechnology companies. A key aspect of the technology is that exploration of chemical space is performed by generating compounds in silico under the constraints of program endpoints, rather than screening libraries of compounds.
Pfizer has been deploying Iktos's generative modeling technology to small-molecule programs. "Pfizer has had an active interest in AI for de novo design and we are excited to work with Iktos to use their AI technology on a number of our programs", said Charlotte Allerton, Head of Medicine Design, Pfizer.
Earlier this year, Iktos released Makya™, its generative AI-driven de novo design software for Multi-Parametric Optimization (MPO), available either as a SaaS platform or for implementation on customer premises or in the customer’s Virtual Private Cloud (VPC). Makya’s user-friendly interface enables it to be used by medicinal or computational chemists. Makya can also be operated as a Python package through a Jupyter notebook interface.
"We are proud to work with Pfizer and to have their scientists use our software in their early discovery programs", said Yann Gaston-Mathé, Co-founder and CEO of Iktos. "It is our ultimate goal to put our technology in the hands of drug discovery scientists, who have deep knowledge and understanding of their discovery programs. By combining their drug discovery expertise with our algorithmic and data science know-how and experience derived from the many collaborations we have established to date, we believe that the promise of AI to dramatically improve drug discovery will have a better chance to be realized and impact therapeutic development".
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