Precision drug discovery
Novel Small Molecule Discovery
At Collective Scientific, we help researchers develop de novo candidates and focus development resources on the most promising compounds to increase productivity and maximize the probability of a successful discovery. We’ve created a modeling-based process to derive ideal chemical structures for solving virtually any disease target without the limitations of the chemical collections used in traditional screening with results delivered within a week. Using cloud-based distributed systems, proprietary algorithms, and machine learning modules, we help discovery teams of all sizes to derive new chemical entities atom by atom. These entities are tailored specifically to a disease and optimized as part of a single automated process.
Get started with a free review of your specific target.
Sometimes a molecular scaffold already exists but is not yet optimized. The same processes that allow us to discover novel structures can be used to optimize the properties of a scaffold. It's like having the resources available to do a structure-activity relationship (SAR) study with hundreds of thousands of combinations.
See if your target and scaffold could benefit with a free review.
Virtual screening, (also known as in silico screening), employs computer simulations of various compounds binding with a target. Classic virtual screening offers an inexpensive alternative to traditional high-throughput screening for a new target. It can also be used as a way to narrow down the libraries needed for a high-throughput screen.
This normally requires a lab to have someone familiar with computer scripting, the available software, and enough computing power to finish in a reasonable time. At Collective Scientific, we can return results within a week without additional training or investments in computing infrastructure.
Price starts at $1,000 per screen.
Molecular Dynamics Simulation
With molecular dynamics simulations, both the ligand and protein are modeled as flexible structures. This can be used to determine if a ligand would be able to reach a suspected binding pocket. Molecular dynamics simulations can also be used to uncover data about the expected motion of a protein to help determine likely binding kinetics.
Nearly all drug discovery simulations require a structure of the given target. In the best case, these exist as experimentally derived crystal or NMR structures available at the Protein Data Bank. If your target does not have a known structure, we can use homology modeling to determine a structure that is similar to the desired target. This unlocks simulation techniques that would be otherwise unavailable.