This workshop covers searching and analysis of structural data in PSILO®, and organization of structural families in MOE-Project. Participants will learn to consolidate macromolecular and protein-ligand structural data, generate and navigate advanced queries, and streamline analysis with 3D contact and protein pocket similarity searches. The session also covers organizing structure-based drug design projects, aligning protein families, configuring MOE-Project databases, and using Protein-Ligand Interaction Fingerprints (PLIF) analysis to compare ligand binding modes. Attendees will gain practical skills for efficient structural data mining and project management.

This workshop focuses on methods for analyzing and optimizing peptide-protein interactions in the binding site. Participants will learn peptide-protein structure preparation, peptide sequence optimization using both natural and non-natural amino acids, and perform conformational analysis. Peptide-protein docking will be demonstrated, along with tools for analyzing protein-ligand interactions to assess contact points. Advanced conformational searching techniques using distance restraints will also be described.

This workshop provides an overview of SVL capabilities and its application within MOE. Participants will be introduced to the essentials of the SVL scripting language, including its use in automating tasks, customizing workflows, and enhancing MOE functionalities for specific research needs.

This workshop focuses on structure-based antibody design. Topics such as protein-protein interaction analysis, in silico protein engineering, affinity modeling, and antibody homology modeling will be covered. Participants will explore an antibody-antigen co-crystallized complex by generating and examining molecular surfaces and visualizing protein-protein contacts in 3D. Antibody properties will be evaluated using specialized protein property descriptors and protein patch analysis. The session will also cover the application of protein engineering tools for affinity and property optimization in the context of antibody developability. Additionally, participants will learn to optimize antibody homology models, including identifying glycosylation sites and their selective modification using a specialized MOE Project antibody database. The full workflow for high-throughput antibody homology modeling, from sequence to structure to property calculations for developability analysis, will be demonstrated.

This workshop explores advanced structure-based drug design (SBDD) workflows used in drug discovery projects. Participants will learn about key topics, including pharmacophore query generation, protein-ligand docking, scaffold replacement, and R-group screening. The session will cover the application of pharmacophore searching in drug design, as well as methods for querying 3D project databases. Additionally, participants will generate and analyze protein-ligand interaction fingerprints (PLIF) to support their design strategies.

The course covers essential in silico methods needed for guiding drug discovery projects in the absence of a protein structure. SAR Analysis through R-group profiling and matched molecular pairs (MMP) analysis using MOEsaic to determine relationships among a chemical series are examined. Molecular descriptor calculations and their application for determining property correlations are described. Molecular alignments and conformational analyses of a congeneric series are explored to assess the impact of ligand substituents. An approach for developing pharmacophore queries is discussed. Management and manipulation of MOE databases are also covered.

This workshop covers essential methods for aligning protein sequences, superposing structures, loop modeling, building fusion protein models, and conducting protein-protein docking. Participants will learn techniques for grafting and refining antibody CDR loops, as well as using a knowledge-based approach to scFv fusion protein modeling with the Linker Modeler application. The session will also cover protein-protein docking of an antibody to an antigen and epitope mapping. Finally, the workshop will guide participants through a complete workflow for generating a QSAR model to predict and analyze protein/biologics solubility.

This workshop explores the suite of MOE applications for small-molecule virtual screening. Participants will learn to prepare small-molecule databases for screening, filter databases using substructure matching and property values, and build QSAR/QSPR models and fingerprint similarity models as screening filters. The session will cover creating and searching pharmacophore queries and performing small-molecule docking. A complete virtual screening workflow will be presented, including the creation of de novo structures using MOE’s Scaffold Replacement and Medchem transformation tools.

This workshop describes cheminformatics methods for managing and analyzing datasets of small molecules, as well as generating quantitative structure-activity relationship (QSAR) models. Participants will learn how to import data, prepare MOE databases, calculate molecular descriptors, and analyze trends using sorting, coloring, and plotting tools. Advanced database filtering, data clustering, and diverse subset selection techniques will also be explored. The workshop will include sessions on developing both linear regression and binary QSAR models. Finally, data mining of large compound libraries will be demonstrated using substructure and molecular fingerprint similarity searching.

Antibody-drug conjugates (ADCs) constitute a rising class of medicines that combine the selectivity of antibodies with the cytotoxicity of drug payloads to yield highly targeted and potent therapeutics. Owing to the need to chemically modify antibody residues for the attachment of the drug payload and their more complex structure compared to either component alone, ADCs can present additional challenges related to stability of the final drug product. This talk explores the use of high-throughput experimental screens and computational techniques to understand the conformational and colloidal behavior of an IgG1-based ADC, representing the first reported example of tuning the biophysical properties (e.g. melting temperature, aggregation temperature, diffusion interaction parameter) of a monomethyl auristatin F-based ADC by screening a large formulation library. The ADC, which exhibits high opalescence and strong attractive protein-protein interactions, is transformed into a more stable structure by experimentally traversing a library of more than ~100 different formulation conditions. A significant reduction in turbidity and diffusion interaction parameter is observed by varying solution attributes such as pH and ionic strength. Molecular modeling simulations rationalized these changes and pointed to the presence of attractive electrostatic interactions between ADC molecules under specific solution conditions (low pH and low ionic strength) facilitated by the drug payload and histidine residues. Experimental characterization of the ADC’s structure using techniques such as circular dichroism, size-exclusion chromatography, and peptide mapping further corroborated the computational results. Taken together, we envision that the studies and findings presented here will be applicable across a wide scope of ADCs towards enhancing their stability, therapeutic efficacy, and ultimate prospects for developability.

IKZF2 degradation represents a promising strategy for the development of a new class of cancer immunotherapy. However, Cereblon-recruiting molecular glues may have undesirable neosubstrate (e.g. CK1a, GSPT1, etc) activity that could lead to liabilities. Here, we present structure-based lead optimization on IKZF2 molecules to mitigate CK1a liability to achieve potent and selective IKZF2 molecular glue degraders. Structural comparisons identified an area for chemistry modifications which may disturb CK1a binding while maintaining IKZF2 activities. The optimized molecules showed high potency and efficacy on IKZF2 with a clean profile against CK1a.

MTAP gene loss, occurring in 10-15% of human cancers, results in cellular build-up of methylthioadenosine (MTA), the substrate of the MTAP protein. A synthetic lethal relationship has been demonstrated between PRMT5 inhibition and MTAP gene loss. PRMT5 inhibitors that bind cooperatively with MTA take advantage of this altered cellular environment to selectively kill MTAP-null vs MTAP-WT cells, a strategy that has now achieved clinical proof of concept. The work described herein focuses on our successful efforts to use structural insights to convert first generation, SAM-cooperative PRMT5 inhibitors into compounds that inhibit PRMT5 preferentially in the presence MTA. Also described are our efforts to advance a modestly potent compound identified in a high-throughput screening campaign into the clinical-stage MTA-cooperative inhibitors TNG908 and TNG462. The role that structural biology played throughout the process will be highlighted.

Structural information of drug targets has proven to be an asset to drug discovery projects. MOE provides a broad platform for disseminating structural information and project data enabling transforming the data into knowledge to move the project forward. Understanding and quantifying protein-ligand interactions is a valuable consideration in molecular design. Assessing the electrostatic match between compounds and binding pockets provides important insights into why compounds bind. In this presentation I will discuss a case study, where we use MOE project and electrostatic complementary at various stages of a project in medicinal chemistry.