Pfizer partners with PostEra to speed up drug discovery using machine learning technology

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In an exciting new development, Pfizer has teamed up with PostEra, a leading biotech company specializing in machine learning solutions for preclinical , to significantly accelerate the process of developing . This collaboration focuses on the innovative use of generative chemistry, powered by PostEra’s machine learning technology, to optimize the design of novel molecular structures. The goal is to create drugs with enhanced potency and better drug-like properties, potentially transforming how the pharmaceutical industry approaches drug development.

Transforming drug discovery with machine learning technology

This partnership taps into the power of machine learning for drug discovery, a growing trend in the industry aimed at overcoming the lengthy and expensive process of preclinical drug development. PostEra’s platform technology offers a faster, more efficient way of designing drugs by not only considering chemical synthesis but also integrating the hypotheses of experienced medicinal chemists. This hybrid approach is expected to streamline the development of new therapeutics, significantly reducing the time it takes to bring potentially life-saving drugs to market.

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As part of this multi-year collaboration, Pfizer will combine its extensive data and expertise with PostEra’s machine learning capabilities to push the boundaries of generative chemistry. This partnership aims to create scalable models that could be implemented in Pfizer’s preclinical drug discovery efforts, enhancing their overall research pipeline and potentially speeding up the availability of innovative treatments.

PostEra stands to benefit from upfront and milestone payments

The agreement sees PostEra receiving an upfront payment from Pfizer, with the possibility of additional payments as the project progresses. Moreover, PostEra will retain ownership of all the algorithms developed during the course of the collaboration, ensuring that the company remains a key player in the development of cutting-edge technology for drug discovery. This deal is not only a testament to the success of PostEra’s machine learning platform, but it also represents a major leap forward in how AI is being used to solve real-world challenges in drug development.

, CEO of PostEra, expressed his excitement about the collaboration, stating, “PostEra is delighted to be working with Pfizer to solve some of the key points in preclinical drug discovery. As a team, we are proud to be entering this collaboration and will be leveraging PostEra’s many advances in machine learning to help Pfizer accelerate progress toward novel therapeutics.”

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Pfizer’s strategic investment in machine learning technologies

Charlotte Allerton, the Head of Medicine Design at Pfizer, shared her views on the importance of this collaboration, saying, “We believe that our investments in machine learning technologies could potentially help us expedite preclinical drug discovery. PostEra’s technology complements this effort, and we look forward to seeing the results of the collaboration.” This statement underscores Pfizer’s commitment to investing in advanced technologies that can speed up the development of new drugs, a crucial step in staying competitive in the fast-paced pharmaceutical industry.

Continuing collaborations to advance drug discovery

The partnership between PostEra and Pfizer is not the only exciting collaboration for the biotech company. Last month, PostEra also entered into a collaboration with NeuroLucent to identify small-molecule therapeutics for Alzheimer’s disease, further cementing its role as a key player in the innovative use of machine learning for drug discovery.

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With this growing focus on machine learning and generative chemistry, the future of drug development looks promising. As collaborations like the one between Pfizer and PostEra continue to unfold, they bring hope for faster, more efficient drug discovery processes that could lead to groundbreaking treatments for a variety of diseases.


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