Schrödinger Japan
Drug Discovery Summit 2024
Abstract

July 2024

The Predict-First paradigm: How Digital Chemistry is
Shaping the Future of Drug Discovery
Aleksey Gerasyuto, PhD
Vice President, Drug Discovery, Head of Chemistry, Schrödinger
Digital chemistry offers a modern paradigm for enabling rapid in silico testing of design ideas
using highly accurate computational assays of key properties, accessible across whole project
teams. This shift from design strategies based largely on experimental trial and error towards a‘predict-first’ approach to drug discovery allows teams to dramatically expand the chemical spacethat can be explored and results in a highly interactive, computationally-driven design-modelmake-test-analyze (DMMTA) cycle. Chemists are empowered to test hypotheses throughpredictive modeling and iteratively improve designs prior to compound synthesis. Teams canconfidently explore novel, and often more complex designs while sending only the top scoring
molecules for synthesis therefore improving efficiency and cost of the discovery process.
In this talk we describe a predict-first approach using prospective physics-based computationalmodels to advance two of our wholly owned oncology programs through successive rounds ofchemical space exploration, filtering, and compound optimization to generate clinical-stagecompounds. We will also present the discovery and optimization of selective aV family integrinfamily inhibitors driven by computational methods and structural biology advancements fromcollaborative work with Morphic Therapeutic.
Our MALT1 inhibitor SGR-1505 was discovered in 10 months from project initiation and shows
potent inhibition of MALT1 enzymatic activity and anti-proliferative activity in the activated B-cell(ABC) subtype of diffuse large B cell lymphoma (DLBCL) cell lines. In combination with approved
agents, SGR-1505 demonstrates strong combination potential with Bruton’s tyrosine kinase(BTK) inhibitors, such as ibrutinib in ABC-DLBCL cell lines. SGR-1505 has recently beencharacterized in human clinical trials. In our WEE1/Myt1 inhibitor program, Free Energy
Perturbation (FEP) and Protein FEP were deployed to identify novel, potent, and highly selective
nanomolar WEE1/Myt1 co-inhibitors that demonstrate superior kinase selectivity compared toexisting WEE1 clinical compounds in a broad kinase panel with >450 kinases (ScanMAX) anddesirable ADME, CYP3A4 TDI properties, and PK profiles in preclinical species. SGR-3515
demonstrates dose-dependent target engagement, tumor growth inhibition, and tumor regression
at high doses and improved therapeutic index after intermittent dosing.
Inhibitors of integrins αvβ6 and αvβ1 have the potential to treat fibrotic disease through blockageof the TGFβ pathway. In collaboration between Morphic Therapeutic and Schrodinger, weinvestigated a series of zwitterionic alpha amino acid αvβ6 inhibitors in a structurally enabledprogram. Efficient DMMTA cycles driven by FEP+ for potency and selectivity predictions and QM
pKa calculations for permeability optimization enabled rapid identification of potent and selectiveαvβ6 inhibitors. These efforts resulted in identification of αvβ6 selective development candidate
MORF-627. We were able to use FEP+ to identify αvβ1 selective inhibitors quickly within thesame zwitterionic alpha amino acid chemical series.

PROteolysis TArgeting Chimeras; PROTACsPROTAC

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in silicoFEPin silicoLeveraging Physics-based Computational Approaches
for the Discovery ofHighly Novel and Potent NLRP3 Inhibitors
Andrew Placzek, PhD
Principal Scientist, Schrödinger
The NLRP3 Inflammasome is a very attractive therapeutic target for addressing a wide variety
of inflammatory and autoimmune diseases. High therapeutic momentum stemming frompositive preclinical and clinical data with NLRP3 inhibitors has given rise to a highly competitive
area of drug discovery involving many biotechnology and pharmaceutical companies. In thistalk, I will describe the use of our physics-based computational platform, allied with machinelearning techniques, enabling the rapid identification of several unique and potent NLRP3inhibitors capable of targeting both peripheral and CNS diseases. A range of functional andmechanistic cellular assays, in vivo pharmacodynamic and human disease models have beenused to explore NLRP3 pathway inhibition by these small molecules.

Leveraging the Ongoing Revolutions inMachine Learning and Physics-Based Modeling toExpanded Impact in Small Molecule andBiologics Drug Discovery
Matt Repasky, PhD
Senior Vice President, Schrödinger

At Schrodinger, the multi-year mission of scientific development has three components, (1) toincrease the number and types of targets our platform can progress, (2) to improve theeffectiveness and efficiency of discovery program progression, and (3) to expand theapplicability of our platform to new, high value areas. To provide insight into the types ofchallenges that are currently under investigation, a few not-yet-released active scientific projects
will be discussed covering small molecule and biologics development. These projects employ a
mixture of physics-based modeling and machine learning to advance the three high-level goalsof scientific development.

In Silico Enabled Hit Identification with
the Schrödinger Platform: Case Studies in EGFR andDLK Inhibitor Drug Discovery Programs
Hideyuki Igawa, PhD
Director, Therapeutic Group, Schrödinger

In this talk, we will present examples of ultra-large scale in silico chemical space
exploration using AutoDesigner, a synthetically-aware de novo design method, coupledwith highly accurate physics based FEP+ calculations. We will outline the application ofthis approach to successful and rapid hit identification in two drug discovery programs
within Schrödinger’s therapeutics group. In the epidermal growth factor receptor (EGFR)
triple mutant program, we will detail a discovery of a novel chemical series with a uniquehinge binding motif and identification of a WT-sparing lead compound with dosedependent tumor growth inhibition in a Ba/F3 EGFR del19/T790M/C797S CDX model. In
the dual leucine zipper kinase (DLK) program, a novel energy of solvation method (E-sol)will also be highlighted to identify brain-penetrant advanced lead molecule (KAI-11101)
which showed neuroprotective properties in an ex vivo axon fragmentation assay anddemonstrated dose-dependent p-c-Jun reduction in in vivo mouse cerebellum PK model.

Exploiting Computational Tools in the Design ofFirst-in-class Small-molecule Inhibitors ofSARS-CoV-2 NSP14 Guanine-N7 RNA Cap
Methyltransferase
David J. Huggins, PhD
Executive Director, Computational Biomedicine
Sanders Tri-Institutional Therapeutics Discovery Institute, Bronk Laboratory
The global impact of the COVID-19 pandemic necessitated the development of new
therapeutics targeting the causative agent, SARS-CoV-2, to reduce hospitalizations anddeaths by lessening the severity of the disease. Disruption of the RNA-capping machineryof SARS-CoV-2 represents a novel therapeutic approach via a mechanism important forviral RNA translation and evasion of the host innate immune response. The first step inthis process is mediated by the viral guanine-N7 methyltransferase (MTase) activity ofNSP14. Here, we report the development of a highly-potent, non-nucleoside, smallmolecule inhibitor of NSP14 MTase activity. High-throughput screening identified asulfonamide-carboxamide pyrrole scaffold exemplified by TDI-012992. We exploited arange of computational tools during hit-to-lead optimization of this series. Prior tostructural enablement, we used large-scale enumeration to efficiently explore chemicalspace. Compound data analysis, ideation, and modelling were coordinated through
Schrödinger’s Live Design platform. In addition, we worked with Schrödinger to implement
third-party tools for exploration of commercially-available chemical space andretrosynthesis planning into Live Design. Using the crystal structures that became
available during the project, we then utilized Schrödinger’s Glide docking and FEP+
calculations to drive compound potency into the subnanomolar range. Design efforts
resulted in the identification of TDI-015051 with a KD of 39 pM and an EC50 of 11 nM ina cell-based viral replication system. TDI-15051 also inhibited viral replication in a mousemodel with efficiency comparable to the FDA-approved protease inhibitor nirmatrelvir.
This finding represents the first validation of NSP14 as a drug target for SARS-CoV-2 andthe first example of a methyltransferase inhibitor as an antiviral. Inhibition of viral cap
methylases as an antiviral strategy should also be applicable to other viruses of pandemic
concern.