Synthetic Access Research Articles

Using a New Pt(II) Source to Make Pt(II) Lantern‐Shaped Cages, Including Low‐Symmetry, Heteroleptic, and Multicavity Examples

Current synthetic methods towards Pt(II) lantern‐shaped cages involve the use of dry solvent, inert atmosphere, lengthy reaction times, and highly variable yields if isolated. Starting materials such as [Pt(CH3CN)4](BF4)2 suffer from a poor shelf‐life, reducing the synthetic accessibility of various Pt(II) architectures. A new Pt(II) source (with varied counterions), [Pt(3‐ClPy)4](X)2 (3‐ClPy = 3‐chloropyridine, X = BF4‐, OTf‐, NO3‐), is developed and characterised, showing greatly enhanced shelf‐life characteristics under ambient atmospheric conditions. Using this starting material, the assembly of Pt(II) lantern‐shaped cages was completed in as little as 1.5 hours with wet solvent and ambient atmosphere. The first examples of low‐symmetry, heteroleptic and multicavity Pt(II) lantern‐shaped cages are reported using this approach. Attempts towards an M6L8 octahedron using a tritopic ligand instead generate an interesting M2L4 cage with unbound pyridyl arms. Ligands and cages are characterised by NMR spectroscopy, IR spectroscopy, mass spectrometry, and X‐ray crystallography in some cases.

Using a NewPt(II) Source to Make Pt(II) Lantern-Shaped Cages, Including Low-Symmetry, Heteroleptic, and Multicavity Examples.

Current synthetic methods towards Pt(II) lantern-shaped cages involve the use of dry solvent, inert atmosphere, lengthy reaction times, and highly variable yields if isolated. Starting materials such as [Pt(CH3CN)4](BF4)2 suffer from a poor shelf-life, reducing the synthetic accessibility of various Pt(II) architectures. A new Pt(II) source (with varied counterions), [Pt(3-ClPy)4](X)2 (3-ClPy = 3-chloropyridine, X = BF4-, OTf-, NO3-), is developed and characterised, showing greatly enhanced shelf-life characteristics under ambient atmospheric conditions. Using this starting material, the assembly of Pt(II) lantern-shaped cages was completed in as little as 1.5 hours with wet solvent and ambient atmosphere. The first examples of low-symmetry, heteroleptic and multicavity Pt(II) lantern-shaped cages are reported using this approach. Attempts towards an M6L8 octahedron using a tritopic ligand instead generate an interesting M2L4 cage with unbound pyridyl arms. Ligands and cages are characterised by NMR spectroscopy, IR spectroscopy, mass spectrometry, and X-ray crystallography in some cases.

Organocatalytic Electrophilic Arene Amination: Rapid Synthesis of 2-Quinolones.

For decades, the synthesis of 2-quinolones, a crucial structural motif in pharmaceuticals and agrochemicals, has relied heavily on costly noble metal complexes and structurally complex ligands. Despite considerable efforts from synthetic chemists, a mild, metal-free, environmentally friendly, and cost-effective approach has remained elusive. This study introduces a robust, metal-free synthetic platform that leverages an innovative organoiodine-catalyzed electrophilic arene C(sp2)-H amination strategy to efficiently produce a wide range of new and modifiable 2-quinolones. Moreover, this study allows ready synthetic access to novel 8-aryl-substituted 2-quinolones, uncovering new chemical spaces with significant potential for medicinal applications.

A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.

Deep generative models are gaining attention in the field of de novo drug design. However, the rational design of ligand molecules for novel targets remains challenging, particularly in controlling the properties of the generated molecules. Here, inspired by the DNA-encoded compound library technique, we introduce DeepBlock, a deep learning approach for block-based ligand generation tailored to target protein sequences while enabling precise property control. DeepBlock neatly divides the generation process into two steps: building blocks generation and molecule reconstruction, accomplished by a neural network and a rule-based reconstruction algorithm we proposed, respectively. Furthermore, DeepBlock synergizes the optimization algorithm and deep learning to regulate the properties of the generated molecules. Experiments show that DeepBlock outperforms existing methods in generating ligands with affinity, synthetic accessibility and drug likeness. Moreover, when integrated with simulated annealing or Bayesian optimization using toxicity as the optimization objective, DeepBlock successfully generates ligands with low toxicity while preserving affinity with the target.

Enantioselective Synthesis of Chiral Sulfonimidoyl Fluorides Facilitates Stereospecific SuFEx Click Chemistry.

Sulfur-centered electrophilic 'warheads' have emerged as key components for chemical proteomic probes through sulfur-exchange chemistry (SuFEx) with protein nucleophiles. Among these functional groups, sulfonimidoyl fluorides (SIFs) stand out for their modifiable sites, tunable electrophilicities, and chiral sulfur-center, presenting exciting possibilities for new covalent chemical probes. However, the synthetic access to chiral SIFs has been a challenge, limiting their exploration and applications. In this study, we describe a convenient route to obtain chiral SIFs from readily available sulfenamides via a series of one-pot tandem reactions with high enantiomeric excess (ees). The resulting chiral SIFs were further converted into a diverse array of chiral S(VI) derivatives under mild conditions or in buffer solutions. Most significantly, the specificity of the chiral SIFs in protein ligation experiments underscored the critical role of sulfur-center chirality in the design and screening of more-selective covalent probes and therapeutics.

Enantioselective Synthesis of Chiral Sulfonimidoyl Fluorides Facilitates Stereospecific SuFEx Click Chemistry

Sulfur‐centered electrophilic 'warheads' have emerged as key components for chemical proteomic probes through sulfur‐exchange chemistry (SuFEx) with protein nucleophiles. Among these functional groups, sulfonimidoyl fluorides (SIFs) stand out for their modifiable sites, tunable electrophilicities, and chiral sulfur‐center, presenting exciting possibilities for new covalent chemical probes. However, the synthetic access to chiral SIFs has been a challenge, limiting their exploration and applications. In this study, we describe a convenient route to obtain chiral SIFs from readily available sulfenamides via a series of one‐pot tandem reactions with high enantiomeric excess (ees). The resulting chiral SIFs were further converted into a diverse array of chiral S(VI) derivatives under mild conditions or in buffer solutions. Most significantly, the specificity of the chiral SIFs in protein ligation experiments underscored the critical role of sulfur‐center chirality in the design and screening of more‐selective covalent probes and therapeutics.

MOFSynth: A Computational Tool toward Synthetic Likelihood Predictions of MOFs.

In the past decade, high-throughput computational studies of materials have increased significantly mainly due to advances in computer capabilities and have attracted a great deal of interest. In the field of metal-organic frameworks (MOFs), over a million hypothetical MOFs have been designed in silico, yet only a small fraction of these have been synthesized. For validating the computational-hypothetical results and accelerating the progress in the field, there is a pressing need for distinguishing MOFs that are more likely to be synthesized for real-life applications. This study presents a comprehensive investigation into the synthesizability likelihood of MOFs, utilizing a novel computational approach based on the disparities in energy and geometry between the linker conformation within the MOF structure and its isolated, free-gas state since both of these have been proven to be critical factors influencing MOF synthesis. Our user-friendly tool streamlines synthesizability evaluation, requiring minimal expertise in computational chemistry. By deconstructing over 40,000 MOFs from databases, including QMOF, CoRE MOF, and ToBaCCo, we analyze key parameters defining the linker strain within the MOF unit cell. Our results indicate that QMOF and CoRE MOF contain more promising candidates for synthesis, while ToBaCCo exhibits a relatively poor synthesizability likelihood due to unoptimized materials. Through extensive analysis, we identify optimal linker candidates for highly synthesizable MOFs. Consistent trends in energy distribution across databases that are confirmed by high Pearson and Spearman coefficients suggest the potential for omitting optimization calculations, significantly reducing computational costs. This study underscores the importance of linker deformation and energy disparities and enhances our understanding of synthetic accessibility in MOF research, offering valuable insights for future advancements in the field.

Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates

ABSTRACTThis paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease.

Separation performances of extended pillar[6]arenes, a new stationary phase for gas chromatography

Pillar[n]arenes (P[n]As, n = 5–10), a new generation of macrocyclic hosts, display interesting properties such as 3D π-electron cavity, host-guest interactions, good stability, and easy functionalization, which make these polymers very promising for separation applications. In particular, extended P[n]As (EP[n]As) show large-sized cavities, structural flexibility, cavity adaptability, and synthetic accessibility. These characteristics have been exploited in this work, which reports for the first time the investigation of two EP[n]As (LP6A-C10 and BpP6A-C10) as stationary phases for gas chromatography (GC). LP6A-C10 and BpP6A-C10 columns exhibited moderate polarity (average polarity 117 and 118) and high column efficiencies (3000 plates/m and 3235 plates/m). These columns achieved complete challenging separation of halogenated benzene, benzaldehyde, phenol, and aniline isomers, which are difficult to resolve due to their high resemblance in structures and properties. Experimental results demonstrate the high selectivity and inertness of the prepared columns and their distinct advantages if compared with commercial HP-5 and HP-35 columns. EP[n]As columns displayed good separation repeatability with RSD values of 0.01%–0.02 % for run-to-run, 0.01%–0.22 % for day-to-day, and 1.36–3.56 % for column-to-column. This work demonstrates the promising future of EP[n]A stationary phases for chromatographic separations.

Coiled Coil Peptide Tiles (CCPTs): Expanding the Peptide Building Block Design with Multivalent Peptide Macrocycles.

Owing to their synthetic accessibility and protein-mimetic features, peptides represent an attractive biomolecular building block for the fabrication of artificial biomimetic materials with emergent properties and functions. Here, we expand the peptide building block design space through unveiling the design, synthesis, and characterization of novel, multivalent peptide macrocycles (96mers), termed coiled coil peptide tiles (CCPTs). CCPTs comprise multiple orthogonal coiled coil peptide domains that are separated by flexible linkers. The constraints, imposed by cyclization, confer CCPTs with the ability to direct programmable, multidirectional interactions between coiled coil-forming "edge" domains of CCPTs and their free peptide binding partners. These fully synthetic constructs are assembled using a convergent synthetic strategy via a combination of native chemical ligation and Sortase A-mediated cyclization. Circular dichroism (CD) studies reveal the increased helical stability associated with cyclization and subsequent coiled coil formation along the CCPT edges. Size-exclusion chromatography (SEC), analytical high-performance liquid chromatography (HPLC), and fluorescence quenching assays provide a comprehensive biophysical characterization of various assembled CCPT complexes and confirm the orthogonal colocalization between coiled coil domains within CCPTs and their designed on-target free peptide partners. Lastly, we employ molecular dynamics (MD) simulations, which provide molecular-level insights into experimental results, as a supporting method for understanding the structural dynamics of CCPTs and their complexes. MD analysis of the simulated CCPT architectures reveals the rigidification and expansion of CCPTs upon complexation, i.e., coiled coil formation with their designed binding partners, and provides insights for guiding the designs of future generations of CCPTs. The addition of CCPTs into the repertoire of coiled coil-based building blocks has the potential for expanding the coiled coil assembly landscape by unlocking new topologies having designable intermolecular interfaces.

De novo design of mIDH1 inhibitors by integrating deep learning and molecular modeling.

Mutations in the IDH1 gene have been shown to be an important driver in the development of acute myeloid leukemia, gliomas and certain solid tumors, which is a promising target for cancer therapy. Bidirectional recurrent neural network (BRNN) and scaffold hopping methods were used to generate new compounds, which were evaluated by principal components analysis, quantitative estimate of drug-likeness, synthetic accessibility analysis and molecular docking. ADME prediction, molecular docking and molecular dynamics simulations were used to screen candidate compounds and assess their binding affinity and binding stability with mutant IDH1 (mIDH1). BRNN and scaffold hopping methods generated 3890 and 3680 new compounds, respectively. The molecules generated by the BRNN performed better in terms of molecular diversity, druggability, synthetic accessibility and docking score. From the 3890 compounds generated by the BRNN model, 10 structurally diverse drug candidates with great docking score were preserved. Molecular dynamics simulations showed that the RMSD of the four systems, M1, M2, M3 and M6, remained stable, with local flexibility and compactness similar to the positive drug. The binding free energy results indicated that compound M1 exhibited the best binding properties in all energy aspects and was the best candidate molecule among the 10 compounds. In present study, compounds M1, M2, M3 and M6 generated by BRNN exhibited optimal binding properties. This study is the first attempt to use deep learning to design mIDH1 inhibitors, which provides theoretical guidance for the design of mIDH1 inhibitors.

Exploring the Potential Antidiabetic Effects of Alkannin (A Naphthoquinone Compound): In Vivo and In Silico Studies

Background It is estimated that the plant has been utilized in India’s traditional medical system for close to 2,500 years. Alkanin is a key active component in Boraginaceae medicinal plant roots. Many scientists are interested in this tiny molecule due to its biological potential. Purpose To examine the hypoglycemic effects of alkannin (a naphthoquinone pigment). Methods Initially, we determined the dosage of alkannin by conducting an acute oral toxicity study per Organisation for Economic Co-operation and Development guidelines. Subsequently, streptozocin was used to induce diabetes in mice, and we chose several parameters such as body weight, food and water intake, blood glucose level, and biochemical estimation for the in vivo evaluation. The enhanced pharmacokinetic features of alkannin, such as its better cytochromes P450 metabolism profiles and higher intestinal absorption as compared to glimepiride, were validated by in silico investigations utilizing Swiss ADME (Absorption, Distribution, Metabolism, and Excretion) and AutoDock Vina. Results In particular, blood glucose levels and body weight in the alkannin-treated group drastically decreased to 138 ± 1.45 mg/dL and 27.9±0.54 g, respectively, as compared with the diabetic-treated group. Moreover, alkaline phosphatase levels dropped to 57.26 ± 3.05 U/L in the alkannin group, suggesting enhanced liver and kidney functions. Creatinine levels also decreased from 1.44 ± 0.10 mg/dL to 0.92 ± 0.09 mg/dL, and serum glutamic oxaloacetic transaminase/aspartate aminotransferase levels decreased from 64.16 ± 3.14 U/L to 47.85 ± 2.01 U/L. Additionally, diabetic controls maintained cholesterol levels between 102.1 ± 11.46 mg/dL and 93.53 ± 2.61 mg/dL. With a considerable binding score of –9.2 kcal/mol for alkannin, molecular docking demonstrated a high degree of binding efficiency and robust interaction with biological targets. The docking results revealed that alkannin exhibits drug-like properties. In addition, it satisfies the lead likeness requirement and has a lower synthetic accessibility score compared to glimepiride. Conclusion The results revealed the promising characteristics and positive outcomes of alkannin, underscoring its potential as a viable candidate for further investigation and prospective advancement as a treatment drug for diabetes.

Accelerated discovery of polymer donors for organic solar cells through machine learning: From library creation to performance forecasting

The design of novel polymer donors for organic solar cells has been a major research focus for decades, but discovering unique materials remains challenging due to the high cost of experimentation. In this study, machine learning models are employed to predict power conversion efficiency (PCE), Mordred descriptors are used for model training. Among the four machine learning models evaluated, the gradient boosting regressor emerged as the best-performing model. Additionally, a chemical library of polymer donors was generated and analyzed using various measures. 30 donors with highest PCE are selected and their synthetic accessibility is evaluated. Similarity analysis has indicated much resemblance in selected polymer donors.

Enhancing De Novo Drug Design across Multiple Therapeutic Targets with CVAE Generative Models.

Drug discovery is a costly and time-consuming process, necessitating innovative strategies to enhance efficiency across different stages, from initial hit identification to final market approval. Recent advancement in deep learning (DL), particularly in de novo drug design, show promise. Generative models, a subclass of DL algorithms, have significantly accelerated the de novo drug design process by exploring vast areas of chemical space. Here, we introduce a Conditional Variational Autoencoder (CVAE) generative model tailored for de novo molecular design tasks, utilizing both SMILES and SELFIES as molecular representations. Our computational framework successfully generates molecules with specific property profiles validated though metrics such as uniqueness, validity, novelty, quantitative estimate of drug-likeness (QED), and synthetic accessibility (SA). We evaluated our model's efficacy in generating novel molecules capable of binding to three therapeutic molecular targets: CDK2, PPARγ, and DPP-IV. Comparing with state-of-the-art frameworks demonstrated our model's ability to achieve higher structural diversity while maintaining the molecular properties ranges observed in the training set molecules. This proposed model stands as a valuable resource for advancing de novo molecular design capabilities.

Aptamer-based Immune Checkpoint Inhibition for Cancer Immunotherapy.

Cancer has long been a significant threat to human life and health. The advent of immune checkpoint blockade strategies has reversed cancer-induced immune suppression, advanced the development of immunotherapy, and offered new hope in the fight against cancer. Aptamers, which possess the same specificity and affinity as antibodies, are advantageous due to their synthetic accessibility and ease of modification, providing novel insights for immune checkpoint research. In this review, we outline the key aptamers currently developed for immune checkpoints such as CTLA-4, PD-1, PD-L1 and Siglec-15. We explore their potential in therapeutic strategies, including functionalizing or engineering aptamers for covalent binding, valency control, and nanostructure assembly, as well as investigating molecular mechanisms such as glycosylated protein functions and cell-cell interactions. Finally, the future applications of aptamers in immunotherapy are discussed.

Navigating Ultralarge Virtual Chemical Spaces with Product-of-Experts Chemical Language Models.

Ultralarge virtual chemical spaces have emerged as a valuable resource for drug discovery, providing access to billions of make-on-demand compounds with high synthetic success rates. Chemical language models can potentially accelerate the exploration of these vast spaces through direct compound generation. However, existing models are not designed to navigate specific virtual chemical spaces and often overlook synthetic accessibility. To address this gap, we introduce product-of-experts (PoE) chemical language models, a modular and scalable approach to navigating ultralarge virtual chemical spaces. This method allows for controlled compound generation within a desired chemical space by combining a prior model pretrained on the target space with expert and anti-expert models fine-tuned using external property-specific data sets. We demonstrate that the PoE chemical language model can generate compounds with desirable properties, such as those that favorably dock to dopamine receptor D2 (DRD2) and are predicted to cross the blood-brain barrier (BBB), while ensuring that the majority of generated compounds are present within the target chemical space. Our results highlight the potential of chemical language models for navigating ultralarge virtual chemical spaces, and we anticipate that this study will motivate further research in this direction. The source code and data are freely available at https://github.com/shuyana/poeclm.

Treatment with a self-expanding endoprosthesis in patients with stenosis or occlusion at the arteriovenous graft: 6-Month outcomes of a post-marketing surveillance study.

To assess 6-month safety and efficacy of a self-expanding stent graft placement to treat stenotic or thrombosed synthetic arteriovenous access graft at the venous anastomoses. This prospective, multicenter, post-market surveillance study assessed the GORE® VIABAHN® Endoprosthesis (stent graft) in Japanese patients with stenotic or thrombosed vascular access grafts. Key efficacy and safety outcomes were primary patency (PP) and secondary patency (SP) of the target lesion and the vascular access circuit, repeat interventions, technical and clinical success, incidence of adverse and serious adverse events, and device deficiencies through 6 months. The 103 enrolled patients (mean age 71.8 years; 57.3% female) had stent grafts placed in the venous anastomosis of arteriovenous access grafts (on-label). The majority of lesions were stenotic (77.1%) and located in the upper arm (91.4%). Mean lesion length was 42.9 ± 23.6 mm and diameter was 6.7 ± 1.9 mm; 28 patients (27.2%) had elephant trunk placements. Technical and clinical success rates were 100%. At 6 months, Kaplan-Meier estimates of target lesion and circuit PP were 75.1% (95% CI: 65.2, 82.6) and 66.8% (95% CI: 56.4, 75.3), and SP rates were 93.8% (95% CI: 86.7, 97.2) and 93.8% (95% CI: 86.7, 97.2), respectively. Mean cumulative number of reinterventions (0.36; 95% CI: 0.20, 0.51) and incidence of device- or procedure-related adverse events were low (⩽3%). No device deficiencies were reported. The 6-month outcomes of the self-expanding stent graft to treat thrombosed venous anastomoses of synthetic hemodialysis vascular access grafts with stenosis demonstrated safety and effectiveness.

Recent Progress in the Synthesis and Glycosylation of Rare Sugars

AbstractOut of 42 naturally occurring monosaccharides, only seven are abundant in Nature (glucose, galactose, mannose, fructose, xylose, ribose, and l-arabinose), while the others have been classified as ‘rare sugars’. Fungi and bacteria use a wide range of monosaccharides, in contrast to mammals, reflected in their glycosylated metabolites, as well as the cellular machineries that are involved in their sugar metabolism. Recognition of the microbiome’s impact on human health has led to increased interest in microbial glycans, as they often mediate interaction between host and microbes. Efficient access to rare sugars and oligosaccharides is necessary to study their roles in Nature, which can provide new pharmacological leads. Furthermore, it enables the synthesis of bioactive glycosylated natural products and congeners. This short review highlights recent progress in the synthesis and the efficient, site- and stereoselective glycosylation of rare sugars. Finally, it provides a recent example where synthetic access to rare sugars has enabled biochemical studies to better understand and interfere with processes in Nature.1 Introduction2 Synthesis of Rare Sugars2.1 Syntheses from Renewable Feedstock2.2 De Novo Syntheses3 Glycosylation3.1 Catalyst-Controlled Glycosylation3.2 One-Pot Iterative Oligosaccharide Synthesis in Solution4 Application in Biochemical Research5 Conclusion

SMILES-based QSAR virtual screening to identify potential therapeutics for COVID-19 by targeting 3CLpro and RdRp viral proteins

The COVID-19 pandemic has prompted the medical systems of many countries to develop effective treatments to combat the high rate of infection and death caused by the disease. Within the array of proteins found in SARS-CoV-2, the 3 chymotrypsin-like protease (3CLpro) holds significance as it plays a crucial role in cleaving polyprotein peptides into distinct functional nonstructural proteins. Meanwhile, RNA-dependent RNA polymerase (RdRp) takes center stage as the key enzyme tasked with replicating the viral genomic RNA within host cells. These proteins, 3CLpro and RdRp, are deemed optimal subjects for QSAR modeling due to their pivotal functions in the viral lifecycle. In this study, SMILES-based QSAR classification models were developed for a dataset of 2377 compounds that were defined as either active or inactive against 3CLpro and RdRp. Pharmacophore (PH4) and QSAR modeling were used for the virtual screening on 60.2 million compounds including ZINC, ChEMBL, Molport, and MCULE databases to identify new potent inhibitors against 3CLpro and RdRp. Then, a filter was established based on typical molecular characteristics to identify drug-like molecules. The molecular docking was also performed to evaluate the binding affinity of 156 AND 51 potential inhibitors to 3CLpro and RdRp, respectively. Among the 15 hits identified based on molecular docking scores, M3, N2, and N4 were identified as promising inhibitors due to their good synthetic accessibility scores (3.07, 3.11, and 3.29 out of 10 for M3, N2, and N4 respectively). These compounds contain amine functional groups, which are known for their crucial role in the binding interactions between drugs and their targets. Consequently, these hits have been chosen for further biological assay studies to validate their activity. They may represent novel 3CLpro and RdRp inhibitors possessing drug-like properties suitable for COVID-19 therapy.

Identification of Novel Tyrosinase Inhibitors with Nanomolar Potency Using Virtual Screening Approaches.

Hyperpigmentation disorders are caused by excess production of the pigment melanin, catalyzed by the enzyme tyrosinase. Novel tyrosinase inhibitors are needed as therapeutic agents to treat these conditions. To discover new inhibitors, we performed a virtual screening of the ZINC20 library containing 1.4 billion compounds. An initial filter for drug-likeness, ADMET properties, and synthetic accessibility reduced the library to 10,217 hits. Quantitative structure-activity relationship (QSAR) modeling of this subset predicted nanomolar inhibitory potency for several chemical scaffolds. Comparative molecular docking studies and rigorous binding energy calculations further prioritized four cysteine-containing dipeptide compounds based on predicted strong binding affinity and mode to tyrosinase. Microsecond-long molecular dynamics simulations provided additional atomistic insights into the stability of inhibitor-enzyme binding interactions. This integrated computational workflow effectively sampled an extremely large chemical space to discover four novel tyrosinase inhibitors with half-maximal inhibitory concentration values below 10 nM. Overall, this demonstrates the power of virtual screening and multi-faceted computational techniques to accelerate the discovery of potent bioactive ligands from massive compound libraries by efficiently sampling chemical space.