MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics.

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Discovery of Two Novel Antiplatelet Clinical Candidates (BMS-986120 and BMS-986141) That Antagonize Protease-Activated Receptor 4.

Protease-activated receptor 4 (PAR4) is a G-protein coupled receptor that is expressed on human platelets and activated by the coagulation enzyme thrombin. PAR4 plays a key role in blood coagulation, and its importance in pathological thrombosis has been increasingly recognized in recent years. Herein, we describe the optimization of a series of imidazothiadiazole PAR4 antagonists to a first-in-class clinical candidate, BMS-986120 (43), and a backup clinical candidate, BMS-986141 (49). Both compounds demonstrated excellent antithrombotic efficacy and minimal bleeding time prolongation in monkey models relative to the clinically important antiplatelet agent clopidogrel and provide a potential opportunity to improve the standard of care in the treatment of arterial thrombosis.

Identification of 2-Pyridinylindole-Based Dual Antagonists of Toll-like Receptors 7 and 8 (TLR7/8).

The toll-like receptors (TLRs) play key roles in activation of the innate immune system. Aberrant activation of TLR7 and TLR8 pathways can occur in the context of autoimmune disorders due to the elevated presence and recognition of self-RNA as activating ligands. Control of this unintended activation via inhibition of TLR7/8 signaling holds promise for the treatment of diseases such as psoriasis, arthritis, and lupus. Optimization of a 2-pyridinylindole series of compounds led to the identification of potent dual inhibitors of TLR7 and TLR8, which demonstrated good selectivity against TLR9 and other family members. The in vitro characterization and in vivo evaluation in rodent pharmacokinetic/pharmacodynamic and efficacy studies of BMS-905 is detailed, along with structural information obtained through X-ray cocrystallographic studies.

Discovery of Non-Nucleotide Small-Molecule STING Agonists via Chemotype Hybridization.

The identification of agonists of the stimulator of interferon genes (STING) pathway has been an area of intense research due to their potential to enhance innate immune response and tumor immunogenicity in the context of immuno-oncology therapy. Initial efforts to identify STING agonists focused on the modification of 2',3'-cGAMP (1) (an endogenous STING activator ligand) and other closely related cyclic dinucleotides (CDNs). While these efforts have successfully identified novel CDNs that have progressed into the clinic, their utility is currently limited to patients with solid tumors that STING agonists can be delivered to intratumorally. Herein, we report the discovery of a unique class of non-nucleotide small-molecule STING agonists that demonstrate antitumor activity when dosed intratumorally in a syngeneic mouse model.

Discovery of Potent and Orally Bioavailable Small Molecule Antagonists of Toll-like Receptors 7/8/9 (TLR7/8/9).

The toll-like receptor (TLR) family is an evolutionarily conserved component of the innate immune system, responsible for the early detection of foreign or endogenous threat signals. In the context of autoimmunity, the unintended recognition of self-motifs as foreign promotes initiation or propagation of disease. Overactivation of TLR7 and TLR9 have been implicated as factors contributing to autoimmune disorders such as psoriasis, arthritis, and lupus. In our search for small molecule antagonists of TLR7/9, 7f was identified as possessing excellent on-target potency for human TLR7/9 as well as for TLR8, with selectivity against other representative TLR family members. Good pharmacokinetic properties and a relatively balanced potency against TLR7 and TLR9 in mouse systems (systems which lack functional TLR8) made this an excellent in vivo tool compound, and efficacy from oral dosing in preclinical models of autoimmune disease was demonstrated.

Discovery of Potent Protease-Activated Receptor 4 Antagonists with in Vivo Antithrombotic Efficacy.

In an effort to identify novel antithrombotics, we have investigated protease-activated receptor 4 (PAR4) antagonism by developing and evaluating a tool compound, UDM-001651, in a monkey thrombosis model. Beginning with a high-throughput screening hit, we identified an imidazothiadiazole-based PAR4 antagonist chemotype. Detailed structure-activity relationship studies enabled optimization to a potent, selective, and orally bioavailable PAR4 antagonist, UDM-001651. UDM-001651 was evaluated in a monkey thrombosis model and shown to have robust antithrombotic efficacy and no prolongation of kidney bleeding time. This combination of excellent efficacy and safety margin strongly validates PAR4 antagonism as a promising antithrombotic mechanism.

Improving Accuracy, Diversity, and Speed with Prime Macrocycle Conformational Sampling.

A novel method for exploring macrocycle conformational space, Prime macrocycle conformational sampling (Prime-MCS), is introduced and evaluated in the context of other available algorithms (Molecular Dynamics, LowModeMD in MOE, and MacroModel Baseline Search). The algorithms were benchmarked on a data set of 208 macrocycles which was curated for diversity from the Cambridge Structural Database, the Protein Data Bank, and the Biologically Interesting Molecule Reference Dictionary. The algorithms were evaluated in terms of accuracy (ability to reproduce the crystal structure), diversity (coverage of conformational space), and computational speed. Prime-MCS most reliably reproduced crystallographic structures for RMSD thresholds >1.0 Å, most often produced the most diverse conformational ensemble, and was most often the fastest algorithm. Detailed analysis and examination of both typical and outlier cases were performed to reveal characteristics, shortcomings, expected performance, and complementarity of the methods.

Dimeric Macrocyclic Antagonists of Inhibitor of Apoptosis Proteins for the Treatment of Cancer.

A series of dimeric macrocyclic compounds were prepared and evaluated as antagonists for inhibitor of apoptosis proteins. The most potent analogue 11, which binds to XIAP and c-IAP proteins with high affinity and induces caspase-3 activation and ultimately cell apoptosis, inhibits growth of human melanoma and colorectal cell lines at low nanomolar concentrations. Furthermore, compound 11 demonstrated significant antitumor activity in the A875 human melanoma xenograft model at doses as low as 2 mg/kg on a q3d schedule.

The discovery of macrocyclic XIAP antagonists from a DNA-programmed chemistry library, and their optimization to give lead compounds with in vivo antitumor activity.

Affinity selection screening of macrocycle libraries derived from DNA-programmed chemistry identified XIAP BIR2 and BIR3 domain inhibitors that displace bound pro-apoptotic caspases. X-ray cocrystal structures of key compounds with XIAP BIR2 suggested potency-enhancing structural modifications. Optimization of dimeric macrocycles with similar affinity for both domains were potent pro-apoptotic agents in cancer cell lines and efficacious in shrinking tumors in a mouse xenograft model.

3D matched pairs: integrating ligand- and structure-based knowledge for ligand design and receptor annotation.

We describe an extension to the matched molecular pairs approach that merges pairwise activity differences with three-dimensional contextual information derived from X-ray crystal structures and binding pose predictions. The incorporation of 3D binding poses allows the direct comparison of structural changes to diverse chemotypes in particular binding pockets, facilitating the transfer of SAR from one series to another. Integrating matched pair data with the receptor structure can also highlight activity patterns within the binding site--for example, "hot spot" regions can be visualized where changes in the ligand structure are more likely to impact activity. The method is illustrated using P38α structural and activity data to generate novel hybrid ligands, identify SAR transfer networks, and annotate the receptor binding site.

Trends in kinase selectivity: insights for target class-focused library screening.

A kinome-wide selectivity screen of >20000 compounds with a rich representation of many structural classes has been completed. Analysis of the selectivity patterns for each class shows that a broad spectrum of structural scaffolds can achieve specificity for many kinase families. Kinase selectivity and potency are inversely correlated, a trend that is also found in a large set of kinase functional data. Although selective and nonselective compounds are mostly similar in their physicochemical characteristics, we identify specific features that are present more frequently in compounds that bind to many kinases. Our results support a scaffold-oriented approach for building compound collections to screen kinase targets.