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Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures 1 – 4 and ultra-large libraries of virtual compounds 5 , 6 . However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries 7 , new approaches to compound screening are needed 8 , 9 . Here we introduce a modular synthon-based approach—V-SYNTHES—to perform hierarchical structure-based screening of a REAL Space library of more than 11 billion compounds. V-SYNTHES first identifies the best scaffold–synthon combinations as seeds suitable for further growth, and then iteratively elaborates these seeds to select complete molecules with the best docking scores. This hierarchical combinatorial approach enables the rapid detection of the best-scoring compounds in the gigascale chemical space while performing docking of only a small fraction (<0.1%) of the library compounds. Chemical synthesis and experimental testing of novel cannabinoid antagonists predicted by V-SYNTHES demonstrated a 33% hit rate, including 14 submicromolar ligands, substantially improving over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources. Synthesis of selected analogues of the best hits further improved potencies and affinities (best inhibitory constant ( K i ) = 0.9 nM) and CB 2 /CB 1 selectivity (50–200-fold). V-SYNTHES was also tested on a kinase target, ROCK1, further supporting its use for lead discovery. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm. V-SYNTHES, a scalable and computationally cost-effective synthon-based approach to compound screening, identified compounds with a high affinity for CB2 and CB1 in a hierarchical structure-based screen of more than 11 billion compounds.

On-demand chemical spaces of drug-like compounds open new horizons in the discovery of ligands and drug candidates for clinically relevant targets, but also expose the scalability of computational screening as a key bottleneck. Recently, we introduced the V-SYNTHES modular screening approach, showing >1000-fold acceleration relative to direct brute-force docking of fully enumerated ultra-large chemical libraries. Initially, the method was based on the early version of Enamine REAL space (11 billion compounds) and validated on two relatively well-characterized targets. Here we present an upgraded V-SYNTHES2 workflow with improved automation features and scalability, expanding to REAL Space of 36 billion readily available compounds, and assessing its performance on new, more challenging targets. V-SYNTHES2 introduces a new geometry-based CapSelect tool that fully automates fragment selection based on docking scores and binding poses of the Minimal Enumeration Library (MEL). The method shows excellent enrichment and binding pose reproducibility in computational benchmarks, including targets with shallow pockets, RNA-binding sites, GPCRs, and phospholipid-binding enzymes. Experimental validation shows the utility of this workflow in prospective screening campaigns for two novel targets. The fully automated V-SYNTHES2 workflow can be deployed on computing clusters or clouds, offering a powerful tool for effective screening of giga-scale chemical spaces.

Cysteinyl leukotriene G protein-coupled receptors, CysLT1R and CysLT2R, regulate bronchoconstrictive and pro-inflammatory effects and play a key role in allergic disorders, cardiovascular diseases, and cancer. CysLT1R antagonists have been widely used to treat asthma disorders, while CysLT2R is a potential target against uveal melanoma. However, very few selective antagonist chemotypes for CysLT receptors are available, and the design of such ligands has proved to be challenging. To overcome this obstacle, we took advantage of recently solved crystal structures of CysLT receptors and an ultra-large Enamine REAL library, representing a chemical space of 680 M readily available compounds. Virtual ligand screening employed 4D docking models comprising crystal structures of CysLT1R and CysLT2R and their corresponding ligand-optimized models. Functional assessment of the candidate hits yielded discovery of five novel antagonist chemotypes with sub-micromolar potencies and the best Ki = 220 nM at CysLT1R. One of the hits showed inverse agonism at the L129Q constitutively active mutant of CysLT2R, with potential utility against uveal melanoma.

Chronic neuroinflammation plays a key role in the progression of Alzheimer’s disease (AD), and the cytosolic calcium-dependent phospholipase A 2 (cPLA 2 ) enzyme is a critical mediator of inflammatory lipid signaling pathways. Here we investigate the therapeutic potential of novel cPLA 2 inhibitors in modulating neuroinflammation in AD. By leveraging the giga-scale V-SYNTHES2 virtual screening in on-demand chemical space and conducting two rounds of optimization for potency and selectivity, we have identified BRI-50460, achieving an IC 50 of 0.88 nM in cellular assays that measure cPLA 2 -mediated arachidonic acid release. In vivo studies revealed favorable brain-to-plasma ratios, highlighting the ability of BRI-50460 to penetrate the central nervous system, modulating neuroinflammatory pathways, and restoring lipid homeostasis. In astrocytes and neurons derived from human induced pluripotent stem cells, BRI-50460 mitigates the effects of amyloid beta 42 oligomers on cPLA 2 activation, tau hyperphosphorylation, and synaptic loss. Our results support that small molecule inhibitors of cPLA 2 can modulate the downstream inflammatory signaling, offering a promising therapeutic strategy for neurodegenerative diseases.

Chronic neuroinflammation plays a key role in the progression of Alzheimer's disease (AD), and the cytosolic calcium-dependent phospholipase A (cPLA ) enzyme is a critical mediator of inflammatory lipid signaling pathways. Here we investigate the therapeutic potential of novel cPLA inhibitors in modulating neuroinflammation in AD. By leveraging the giga-scale V-SYNTHES2 virtual screening in on-demand chemical space and conducting two rounds of optimization for potency and selectivity, we have identified BRI-50460, achieving an IC of 0.88 nM in cellular assays that measure cPLA -mediated arachidonic acid release. In vivo studies revealed favorable brain-to-plasma ratios, highlighting the ability of BRI-50460 to penetrate the central nervous system, modulating neuroinflammatory pathways, and restoring lipid homeostasis. In astrocytes and neurons derived from human induced pluripotent stem cells, BRI-50460 mitigates the effects of amyloid beta 42 oligomers on cPLA activation, tau hyperphosphorylation, and synaptic loss. Our results support that small molecule inhibitors of cPLA can modulate the downstream inflammatory signaling, offering a promising therapeutic strategy for neurodegenerative diseases.

Predictive modeling of chemical processes in silico is a goal of XXI century. While robust and accurate methods exist for ground-state properties, reliable methods for excited states are still lacking and require further development. Electronically exited states are formed by interactions of matter with light and are responsible for key processes in solar energy harvesting, vision, artificial sensors, and photovoltaic applications. The greatest challenge to overcome on our way to a quantitative description of light-induced processes is accurate inclusion of the effect of the environment on excited states. All above mentioned processes occur in solution or solid state. Yet, there are few methodologies to study excited states in condensed phase. Application of highly accurate and robust methods, such as equation-of-motion coupled-cluster theory EOM-CC, is limited by a high computational cost and scaling precluding full quantum mechanical treatment of the entire system. In this thesis we present successful application of the EOM-CC family of methods to studies of excited states in liquid phase and build hierarchy of models for inclusion of the solvent effects. In the first part of the thesis we show that a simple gasphase model is sufficient to quantitatively analyze excited states in liquid benzene, while the latter part emphasizes the importance of explicit treatment of the solvent molecules in the case of glycine in water solution. In chapter 2, we use a simple dimer model to describe exciton formation in liquid and solid benzene. We show that sampling of dimer structures extracted from the liquid benzene is sufficient to correctly predict exited-state properties of the liquid. Our calculations explain experimentally observed features, which helped to understand the mechanism of the excimer formation in liquid benzene. Furthermore, we shed light on the difference between dimer configurations in the first solvation shell of liquid benzene and in unit cell of solid benzene and discussed the impact of these differences on the formation of the excimer state. In chapter 3, we present a theoretical approach for calculating core-level states in condensed phase. The approach is based on EOM-CC and effective fragment potential (EFP) method. By introducing an approximate treatment of double excitations in the EOM-CCSD (EOM-CC with single and double substitutions) ansatz, we addressed poor convergence issues that are encountered for the core-level states and significantly reduced computational costs. While the approximations introduce relatively large errors in the absolute values of transition energies, the errors are systematic. Consequently, chemical shifts, changes in ionization energies relative to the reference systems, are reproduced reasonably well. By using different protonation forms of solvated glycine as a benchmark system, we showed that our protocol is capable of reproducing the experimental chemical shifts with a quantitative accuracy. The results demonstrate that chemical shifts are very sensitive to the solvent interactions and that explicit treatment of solvent, such as EFP, is essential for achieving quantitative accuracy. In chapter 4, we outline future directions and discuss possible applications of the developed computational protocol for prediction of core chemical shifts in larger systems.