Psivant Therapeutics is advancing a paradigm shift in biotechnology by pioneering a physics-driven approach to drug design that is tightly coupled to chemistry and biology research and development. Our QUAISAR platform is purpose-built to discover novel small molecule therapeutics to address biologically and genetically validated, but previously intractable, protein targets.
Simulations identify binding hotspots, induced pockets, and optimizable regions. All hypotheses are tracked for measuring progress toward TPP and continual improvements.
Reaction-based enumeration, molecular breeding, and generative modeling explore chemical space based on a specified hypothesis. Our curated chemical building block database ensures that the chemical constituents can be purchased to synthesize the enumerated molecules.
Enumerated chemical space is reduced based on drug-likeness and chemical similarity. These fast calculations eliminate the most unsuitable molecules.
In-house QSAR models using proprietary curated datasets predict ADME properties such as solubility, permeability, hERG, and clearance. Only the most suitable molecules progress past this stage.
Docking, shape overlap, and contact maps account for general fit to the protein pocket and desirable interactions based on design hypotheses.
Short MD simulations and MM-GBSA account for light protein flexibility, protein-ligand interactions, and desolvation. This step eliminates enough molecules for us to move into rigorous free energy simulations.
Rigorous binding free energy simulations account fully for the thermodynamic contributions to binding (enthalpy and entropy). Additionally, advanced MD techniques are applied when long-timescale motions are involved, such as allosteric modulation and signaling.
Molecules are prioritized based on a multi-parameter analysis guided by the TPP. Synthetic accessibility, time to results, and costs are also accounted for. Synthesized compounds are progressed through the assay cascade.
Data is collected, analyzed, and discussed to determine the focus of the next iteration. All data is tracked in our Project Dashboard, with real-time monitoring and reporting of project data. Progression toward TPP is visible through our organization to ensure transparent data-driven decisions.
Psivant Therapeutics has a differentiated pipeline of targets with high biological and disease validation that have historically provided challenges for the design of therapeutics.
Quantum mechanics is the most accurate way to simulate the behavior of molecules and interactions between molecules.
We use statistical thermodynamics to compute biologically relevant quantities such as binding affinity, selectivity, allostery, membrane permeability and solubility.
Molecular Simulation allows us to predict interactions, energies and the conformational behavior of our drug discovery targets.
Artificial intelligence (AI) and machine learning (ML) at Psivant leverage data and cutting-edge algorithms to solve real-world problems in drug discovery.
Our dedicated, built-for-purpose, super-computing cluster provides the computing power necessary to deliver the most accurate results at scale.
Members of our senior science team are the lead authors for some of the most innovative and frequently cited publications appearing in prestigious industry journals.