In late 2019, Schrödinger and AstraZeneca teamed up with the aim of accelerating the discovery of novel therapeutic compounds by improving the throughput of binding affinity predictions. After a successful pilot study, the technology and workflows are now rolled out across AstraZeneca’s small molecule discovery programs.
By augmenting traditional drug design with sophisticated computational methods to predict what molecules to make next and how to make them, this partnership will ultimately enable us to bring better medicines to patients faster.
Read on to hear our perspectives on this new approach to drug discovery:
Anna Åsberg, Vice President, R&D IT, AstraZeneca
Making, testing and analyzing compounds is both resource- and time-intensive. At AstraZeneca, we have made a decision to put digital technology at the heart of our strategy to accelerate this process.
Schrödinger has developed a computational platform to integrate active learning with their physics-based predictive modeling tools such as FEP. This enables the rapid optimization of molecules with regards to binding affinity – an important factor in the design of new compounds – and was an attractive approach to include into our drug discovery projects.
In 2020, we ran a pilot study on more than 10 small molecule projects. We demonstrated a 10-100 fold increase of throughput in the number of FEP calculations as compared to what we could run without combining active learning into FEP (AL-FEP). This increase allowed us to assess and evaluate potential compounds far more accurately and quickly, reducing the number of compounds synthesized in the laboratory.
Werngard Czechtizky, Head of Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, AstraZeneca
For our pilot program, we worked on a variety of different targets. The targets were structurally enabled – a prerequisite for using Schrödinger’s AL-FEP platform effectively. The designs resulting from the Schrödinger platform calculations are used to guide which compounds we synthesize for each project, which saves time and money.
We identified improved and/or novel ligands for half of the targets, including some that traditionally have been difficult to drug. Right now, we are already moving several of these projects through preclinical milestones.
The promising results have led us to extend the collaboration by another three years to fully harness the AL-FEP platform within our computational design. We now aim to use this approach to drive value across all small molecule portfolio projects that are based on structurally-enabled targets.
Robert Abel, Ph.D., Executive Vice President and Chief Computational Scientist, Schrödinger
This collaboration is another important proof point demonstrating that our platform truly has the potential to transform the discovery of new medicines.
We’ve seen the impact our platform can have on our own internal pipeline, in the biotech companies we have co-founded and in our collaborations with global pharmas around specific targets and indications. Now, we are seeing it deployed on a larger scale by a global leader in integrating cutting-edge digital technology into drug discovery.
The success of this collaboration is very gratifying – but it’s important to note that we’re not stopping here. Continued innovation is woven into our DNA at Schrödinger. We invest heavily in R&D to improve and refine our platform and to extend its utility to new targets. To that end, we established an additional collaboration with AstraZeneca in March 2020 to refine our computational platform for biologics. We’re hopeful that we can match the success we’ve shown in the small molecule realm into the design of novel antibodies and protein-based therapeutics.
This is an exciting time for computation-driven drug discovery. We’re thrilled to be working with AstraZeneca, an excellent partner in the quest to accelerate and improve the search for novel therapies, and we look forward to seeing what else we can achieve together.