Hit to development candidate in 10 months: Rapid discovery of a novel, potent MALT1 inhibitor

Scale and accuracy of digital assays drives efficient DMTA cycles

Finding a novel molecule with the right balance of on-target affinity and desired physicochemical properties is the essential challenge of every drug discovery program. In principle, increasing the number of rationally designed compounds assessed across these various properties increases the odds of success. Designing molecules in silico — with the speed and accuracy to traverse billions of molecules — is the guiding ethos of Schrödinger’s digital chemistry strategy. Specifically, this project combines rigorous physics-based modeling with machine learning (ML), predictive ADMET models, and data analytics to search and triage a chemical space consisting of more than 8B compounds. Ultimately, execution of this strategy enabled the identification of multiple novel series.

First, the team performed structure-activity relationship (SAR) analysis of existing chemical matter, followed by computational assessment of the allosteric binding site using WaterMap. As a result, the team identified a number of displaceable high- energy water molecules in regions of the binding site that provided an opportunity to gain potency while exploring different chemotypes.¹

Schrödinger’s drug discovery team used this information to drive the evaluation of billions of compounds via a De Novo Design strategy for iterative large-scale design and scoring. This strategy included synthetically-aware, reaction-based enumeration, crowdsourced medicinal chemistry ideation, and FEP+ for free energy perturbation modeling. The accuracy and utility of FEP+ as a computational assay for the prediction of relative binding energies of molecules has been validated extensively, generating predictions within one kcal/mol of experimental values on average.² By combining FEP+ with high performance cloud computing and machine learning (Active Learning FEP+), over 1,700 molecules were evaluated in the first three months of the project. All ideas and corresponding modeled data crowdsourced by the team were captured and analyzed with LiveDesign, a best-in-class, modeling- enabled collaborative enterprise platform for real-time project ideation (Figure 1). In less than three months, with fewer than 50 total compounds synthesized, the team was able to identify two novel and distinct series of highly potent MALT1 inhibitors, affording progression to in vivo testing.

Overcoming the MPO challenge by tuning potency, solubility, and permeability simultaneously

Once potent chemical series were identified, the team focused on tuning physicochemical properties to meet the target product profile (TPP). Specifically, they employed a single multi-parameter optimization (MPO) score to triage molecules rapidly based on their predicted ability to satisfy the TPP. Calculation of the MPO score was based on values derived from predictive models for solubility, permeability, selectivity, and potency. Using this strategy the design team assessed over 5,000 ideas and identified 43 compounds that met the program’s criteria. A handful progressed to synthesis and experimental testing, reducing cost and time significantly.

Within 10 months and a total of 78 compounds synthesized in the lead series (and 129 compounds program wide), the project team identified a potential best- in-class MALT1 inhibitor with balanced properties and on- target activity, SGR-1505 (Figure 2). SGR-1505 demonstrates strong positive effects in patient-derived B-cell tumor models both as a single agent as well as in combination with existing standard of care (Figure 3).