Challenge
A biotech start-up that specializes in therapeutically targeting proteins with specific structural features faced the daunting task of efficiently selecting and prioritizing potential targets. The complexity of this challenge necessitated the development of a sophisticated in silico target prioritization platform. This platform had to seamlessly integrate multi-omics and disease association data, along with protein structure predictions, on a large scale using academic tools—a considerable undertaking.
Action
Recognizing the firm’s reputation for customized analyses, the client engaged Diamond Age (DA) to collaboratively design and build a comprehensive in silico target selection and prioritization platform. DA assembled a team of senior scientists and engineers, bringing expertise in genomics, proteomics, metabolomics, data modeling and cloud engineering. The DA team worked closely with the client’s internal scientists and academic advisors, who provided in-depth knowledge on the biological impact of structural features and the company’s therapeutic strategy.
DA built a scalable cloud system for running simulations, and integrated the public data and protein structure predictions into a user-friendly dashboard, which empowered the client to effortlessly search and prioritize protein targets for further development.
Result
The synergy between DA’s technical prowess and the client’s domain expertise ensured the development of a tailor-made solution aligned with the company’s specific challenges and goals. By combining diverse datasets and leveraging advanced predictive modeling, the platform emerged as a powerful tool for streamlining the identification and prioritization of potential therapeutic targets. This not only addressed the initial challenge of target selection, but also positioned the company for continued success in drug discovery. This platform played a pivotal role in the selection of one of the client’s current drug programs.