DeepCubist: Molecular Generator for Designing Peptidomimetics based on Complex three-dimensional scaffolds.

Mimicking bioactive conformations of peptide segments involved in the formation of protein-protein interfaces with small molecules is thought to represent a promising strategy for the design of protein-protein interaction (PPI) inhibitors. For compound design, the use of three-dimensional (3D) scaffolds rich in sp3-centers makes it possible to precisely mimic bioactive peptide conformations. Herein, we introduce DeepCubist, a molecular generator for designing peptidomimetics based on 3D scaffolds. Firstly, enumerated 3D scaffolds are superposed on a target peptide conformation to identify a preferred template structure for designing peptidomimetics. Secondly, heteroatoms and unsaturated bonds are introduced into the template via a deep generative model to produce candidate compounds. DeepCubist was applied to design peptidomimetics of exemplary peptide turn, helix, and loop structures in pharmaceutical targets engaging in PPIs.

Regulation of CD28 binding to SH2 domains of Grb2 and PI3K by trisubstituted carboranes for T-cell activation.

Binding of adaptor molecules, such as growth factor receptor-bound protein 2 (Grb2) and phosphoinositide 3-kinase (PI3K), to the cytoplasmic region of CD28 is critical for T-cell activation. The Src homology 2 (SH2) domains of Grb2 and PI3K interact with the cytoplasmic region, including phosphorylated Tyr, of CD28. We found that trisubstituted carboranes efficiently increased the proliferation of T cells obtained from C57BL/6 mice. The carboranes specifically increased the binding of Grb2 Src homology 2 (SH2) to CD28-derived phosphopeptide but decreased the binding of PI3K C-terminal SH2 (cSH2). Based on the crystal structures of CD28-derived phosphopeptides complexed with Grb2 SH2 and PI3K cSH2, the bound structures of compound 4 (CRL266481) were modeled to determine the molecular mechanism of the regulation.

DeepAS - Chemical language model for the extension of active analogue series.

In medicinal chemistry, hit-to-lead and lead optimization efforts produce analogue series (ASs), the analysis of which is of central relevance for the exploration and exploitation of structure-activity relationships (SARs) and generation of candidate compounds. The key question in any chemical optimization effort is which analogue(s) to generate next, for which computational support is typically provided through QSAR analysis and compound potency predictions. In this study, we introduce a new chemical language model for analogue design via deep learning. For this purpose, ASs comprising active compounds are ordered according to increasing potency and the chemical language model predicts preferred R-groups for new analogues on the basis of ordered R-group sequences. Hence, consistent with the principles of deep models for natural language processing, analogues with new R-groups are predicted based upon conditional probabilities taking preceding groups into account. This implicitly accounts for the potency gradient captured by an AS and detectable SAR trends, providing a new concept for analogue design. Herein, we report the AS-based chemical language model, its initial evaluation, and exemplary applications.

Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling.

Deep machine learning is expanding the conceptual framework and capacity of computational compound design, enabling new applications through generative modeling. We have explored the systematic design of covalent protein kinase inhibitors by learning from kinome-relevant chemical space, followed by focusing on an exemplary kinase of interest. Covalent inhibitors experience a renaissance in drug discovery, especially for targeting protein kinases. However, computational design of this class of inhibitors has thus far only been little investigated. To this end, we have devised a computational approach combining fragment-based design and deep generative modeling augmented by three-dimensional pharmacophore screening. This approach is thought to be particularly relevant for medicinal chemistry applications because it combines knowledge-based elements with deep learning and is chemically intuitive. As an exemplary application, we report for Bruton's tyrosine kinase (BTK), a major drug target for the treatment of inflammatory diseases and leukemia, the generation of novel candidate inhibitors with a specific chemically reactive group for covalent modification, requiring only little target-specific compound information to guide the design efforts. Newly generated compounds include known inhibitors and characteristic substructures and many novel candidates, thus lending credence to the computational approach, which is readily applicable to other targets.

A 3-styrylchromone converted from trimebutine 3D pharmacophore possesses dual suppressive effects on RAGE and TLR4 signaling pathways.

Receptor for advanced glycation end-products (RAGE) and Toll-like receptors (TLRs) are potential therapeutic targets in the treatment of acute and chronic inflammatory diseases. We previously reported that trimebutine, a spasmolytic drug, suppresses RAGE pro-inflammatory signaling pathway in macrophages. The aim of this study was to convert trimebutine to a new small molecule using in silico 3D pharmacophore similarity search, and dissect the mechanistic anti-inflammatory basis. Of note, a unique 3-styrylchromone (3SC), 7-methoxy-3-trimethoxy-SC (7M3TMSC), converted from trimebutine 3D pharmacophore potently suppressed both high mobility group box 1-RAGE and lipopolysaccharide-TLR4 signaling pathways in macrophage-like RAW264.7 cells. More importantly, 7M3TMSC inhibited the phosphorylation of extracellular signaling-regulated kinase 1 and 2 (ERK1/2) and downregulated the production of cytokines, such as interleukin-6. Furthermore, 3D pharmacophore-activity relationship analyses revealed that the hydrogen bond acceptors of the trimethoxy groups in a 3-styryl moiety and the 7-methoxy-group in a chromone moiety in this compound are significant in the dual anti-inflammatory activity. Thus, 7M3TMSC may provide an important scaffold for the development of a new type of anti-inflammatory dual effective drugs targeting RAGE/TLR4-ERK1/2 signaling.

Generalized-ensemble method study: A helix-mimetic compound inhibits protein-protein interaction by long-range and short-range intermolecular interactions.

A heterocyclic compound mS-11 is a helix-mimetic designed to inhibit binding of an intrinsic disordered protein neural restrictive silence factor/repressor element 1 silencing factor (NRSF/REST) to a receptor protein mSin3B. We apply a generalized ensemble method, multi-dimensional virtual-system coupled molecular dynamics developed by ourselves recently, to a system consisting of mS-11 and mSin3B, and obtain a thermally equilibrated distribution, which is comprised of the bound and unbound states extensively. The lowest free-energy position of mS-11 coincides with the NRSF/REST position in the experimentally-determined NRSF/REST-mSin3B complex. Importantly, the molecular orientation of mS-11 is ordering in a wide region around mSin3B. The resultant binding scenario is: When mS-11 is distant from the binding site of mSin3B, mS-11 descends the free-energy slope toward the binding site maintaining the molecular orientation to be advantageous for binding. Then, finally a long and flexible hydrophobic sidechain of mS-11 fits into the binding site, which is the lowest-free-energy complex structure inhibiting NRSF/REST binding to mSin3B.

Synthesis of Three-Dimensional (Di)Azatricyclododecene Scaffold and Its Application to Peptidomimetics.

A novel sp3 carbon-rich tricyclic 3D scaffold-based peptide mimetic compound library was constructed to target protein-protein interactions. Tricyclic framework 7 was synthesized from 9-azabicyclo[3,3,1]nonan-3-one (11) via a gold(I)-catalyzed Conia-ene reaction. The electron-donating group on the pendant alkyne of cyclization precursor 12 b-e was the key to forming 6-endo-dig cyclized product 7 with complete regioselectivity. Using the synthetic strategy for regioselective construction of bridged tricyclic framework 7, a diazatricyclododecene 3D-scaffold 8 a, which enables the introduction of substituents into the scaffold to mimic amino acid side chains, was designed and synthesized. The peptide mimetics 21 a-u were synthesized via step-by-step installation of three substituents on diazatricyclododecene scaffold 8 a. Compounds 21 a-h were synthesized as α-helix peptide mimics of hydrophobic ZZxxZ and ZxxZZ sequences (Z=Leu or Phe) and subjected to cell-based assays: antiproliferative activity, HIF-1 transcriptional activity which is considered to affect cancer malignancy, and antiviral activity against rabies virus. Compound 21 a showed the strongest inhibitory activity of HIF-1 transcriptional activity (IC50 =4.1±0.8 µM), whereas compounds 21 a-g showed antiviral activity with IC50 values of 4.2-12.4 µM, suggesting that the 3D-scaffold 8 a has potential as a versatile peptide mimic skeleton.

Trimebutine suppresses Toll-like receptor 2/4/7/8/9 signaling pathways in macrophages.

Because of the critical roles of Toll-like receptors (TLRs) and receptor for advanced glycation end-products (RAGE) in the pathophysiology of various acute and chronic inflammatory diseases, continuous efforts have been made to discover novel therapeutic inhibitors of TLRs and RAGE to treat inflammatory disorders. A recent study by our group has demonstrated that trimebutine, a spasmolytic drug, suppresses the high mobility group box 1‒RAGE signaling that is associated with triggering proinflammatory signaling pathways in macrophages. Our present work showed that trimebutine suppresses interleukin-6 (IL-6) production in lipopolysaccharide (LPS, a stimulant of TLR4)-stimulated macrophages of RAGE-knockout mice. In addition, trimebutine suppresses the LPS-induced production of various proinflammatory cytokines and chemokines in mouse macrophage-like RAW264.7 cells. Importantly, trimebutine suppresses IL-6 production induced by TLR2-and TLR7/8/9 stimulants. Furthermore, trimebutine greatly reduces mortality in a mouse model of LPS-induced sepsis. Studies exploring the action mechanism of trimebutine revealed that it inhibits the LPS-induced activation of IL-1 receptor-associated kinase 1 (IRAK1), and the subsequent activations of extracellular signal-related kinase 1/2 (ERK1/2), c-Jun N-terminal kinase (JNK), and nuclear factor-κB (NF-κB). These findings suggest that trimebutine exerts anti-inflammatory effects on TLR signaling by downregulating IRAK1‒ERK1/2‒JNK pathway and NF-κB activity, thereby indicating the therapeutic potential of trimebutine in inflammatory diseases. Therefore, trimebutine can be a novel anti-inflammatory drug-repositioning candidate and may provide an important scaffold for designing more effective dual anti-inflammatory drugs that target TLR/RAGE signaling.

Addition of hydrophobic side chains improve the apoptosis inducibility of the human glyoxalase I inhibitor, TLSC702.

Glyoxalase I (GLO I) is a known therapeutic target in cancer. Even though TLSC702, a GLO I inhibitor that we discovered, induces apoptosis in tumor cells, exceptionally higher doses are required compared with those needed to inhibit GLO I activity in vitro. In this work, structure-activity optimization studies were conducted on four sections of the TLSC702 molecule to determine the partial structural features necessary for the inhibition of GLO I. Herein, we found that the carboxy group in TLSC702 was critical for binding with the divalent zinc at the active site of GLO I. In contrast, the side chain substituents in the meta- and para- positions of the benzene ring had little influence on the in vitro inhibition of GLO I. The CLogP values of the TLSC702 derivatives showed a positive correlation with the antiproliferative effects on NCI-H522 cells. Thus, two derivatives of TLSC702, which displayed either high or low lipophilicity due to the types of substituents at the phenyl position, were selected. Even though both derivatives showed comparable inhibitory effects as that of their parent compound, the derivative with the high CLogP value was distinctly more antiproliferative than TLSC702. In contrast, the derivative with the low CLogP value did not decrease cell viability in NCI-H522 and HL-60 cells. These findings suggested that structural improvements, such as the addition of hydrophobic moieties to the phenyl group, enhanced the ability of TLSC702 to induce apoptosis by increasing cell membrane permeability.

Adapting the DeepSARM approach for dual-target ligand design.

The structure-activity relationship (SAR) matrix (SARM) methodology and data structure was originally developed to extract structurally related compound series from data sets of any composition, organize these series in matrices reminiscent of R-group tables, and visualize SAR patterns. The SARM approach combines the identification of structural relationships between series of active compounds with analog design, which is facilitated by systematically exploring combinations of core structures and substituents that have not been synthesized. The SARM methodology was extended through the introduction of DeepSARM, which added deep learning and generative modeling to target-based analog design by taking compound information from related targets into account to further increase structural novelty. Herein, we present the foundations of the SARM methodology and discuss how DeepSARM modeling can be adapted for the design of compounds with dual-target activity. Generating dual-target compounds represents an equally attractive and challenging task for polypharmacology-oriented drug discovery. The DeepSARM-based approach is illustrated using a computational proof-of-concept application focusing on the design of candidate inhibitors for two prominent anti-cancer targets.

Development of curcumin-based amyloid ß aggregation inhibitors for Alzheimer's disease using the SAR matrix approach.

Amyloid ß (Aß) aggregation inhibitor activity cliff involving a curcumin structure was predicted using the SAR Matrix method on the basis of 697 known Aß inhibitors from ChEMBL (data set 2487). Among the compounds predicted, compound B was found to possess approximately 100 times higher inhibitory activity toward Aß aggregation than curcumin. TEM images indicate that compound B induced the shortening of Aß fibrils and increased the generation of Aß oligomers in a concentration dependent manner. Furthermore, compound K, in which the methyl ester of compound B was replaced by the tert-butyl ester, possessed low cytotoxicity on N2A cells and attenuated Aß-induced cytotoxicity, indicating that compound K would have an ability for preventing neurotoxicity caused by Aß aggregation.

Design and synthesis of 14 and 15-membered macrocyclic scaffolds exhibiting inhibitory activities of hypoxia-inducible factor 1α.

Inspired by the privileged molecular skeletons of 14- and 15-membered antibiotics, we adopted a relatively unexplored synthetic approach that exploits alkaloidal macrocyclic scaffolds to generate modulators of protein-protein interactions (PPIs). As mimetics of hot-spot residues in the α-helices responsible for the transcriptional regulation, three hydrophobic sidechains were displayed on each of the four distinct macrocyclic scaffolds generating diversity of their spatial arrangements. Modular assembly of the building blocks followed by ring-closing olefin metathesis reaction and subsequent hydrogenation allowed concise and divergent synthesis of scaffolds 1-4. The 14-membered alkaloidal macrocycles 2-4 demonstrated similar inhibition of hypoxia-inducible factor (HIF)-1α transcriptional activities (IC50 between 8.7 and 10 µM), and 4 demonstrated the most potent inhibition of cell proliferation in vitro (IC50 = 12 µM against HTC116 colon cancer cell line). A docking model suggested that 4 could mimic the LLxxL motif in HIF-1α, in which the three sidechains are capable of matching the spatial arrangements of the protein hot-spot residues. Unlike most of the stapled peptides, the 14-membered alkaloidal scaffold has a similar size to the α-helix backbone and does not require additional atoms to induce α-helix mimetic structure. These experimental results underscore the potential of alkaloidal macrocyclic scaffolds featuring flexibly customizable skeletal, stereochemical, substitutional, and conformational properties for the development of non-peptidyl PPI modulators targeting α-helix-forming consensus sequences responsible for the transcriptional regulation.

Prediction of Molecular Properties Using Molecular Topographic Map.

Prediction of molecular properties plays a critical role towards rational drug design. In this study, the Molecular Topographic Map (MTM) is proposed, which is a two-dimensional (2D) map that can be used to represent a molecule. An MTM is generated from the atomic features set of a molecule using generative topographic mapping and is then used as input data for analyzing structure-property/activity relationships. In the visualization and classification of 20 amino acids, differences of the amino acids can be visually confirmed from and revealed by hierarchical clustering with a similarity matrix of their MTMs. The prediction of molecular properties was performed on the basis of convolutional neural networks using MTMs as input data. The performance of the predictive models using MTM was found to be equal to or better than that using Morgan fingerprint or MACCS keys. Furthermore, data augmentation of MTMs using mixup has improved the prediction performance. Since molecules converted to MTMs can be treated like 2D images, they can be easily used with existing neural networks for image recognition and related technologies. MTM can be effectively utilized to predict molecular properties of small molecules to aid drug discovery research.

Computational Prediction of Compound-Protein Interactions for Orphan Targets Using CGBVS.

A variety of Artificial Intelligence (AI)-based (Machine Learning) techniques have been developed with regard to in silico prediction of Compound-Protein interactions (CPI)-one of which is a technique we refer to as chemical genomics-based virtual screening (CGBVS). Prediction calculations done via pairwise kernel-based support vector machine (SVM) is the main feature of CGBVS which gives high prediction accuracy, with simple implementation and easy handling. We studied whether the CGBVS technique can identify ligands for targets without ligand information (orphan targets) using data from G protein-coupled receptor (GPCR) families. As the validation method, we tested whether the ligand prediction was correct for a virtual orphan GPCR in which all ligand information for one selected target was omitted from the training data. We have specifically expressed the results of this study as applicability index and developed a method to determine whether CGBVS can be used to predict GPCR ligands. Validation results showed that the prediction accuracy of each GPCR differed greatly, but models using Multiple Sequence Alignment (MSA) as the protein descriptor performed well in terms of overall prediction accuracy. We also discovered that the effect of the type compound descriptors on the prediction accuracy was less significant than that of the type of protein descriptors used. Furthermore, we found that the accuracy of the ligand prediction depends on the amount of ligand information with regard to GPCRs related to the target. Additionally, the prediction accuracy tends to be high if a large amount of ligand information for related proteins is used in the training.

Trimebutine attenuates high mobility group box 1-receptor for advanced glycation end-products inflammatory signaling pathways.

We previously identified papaverine as an inhibitor of receptor for advanced glycation end-products (RAGE) and showed its suppressive effect on high mobility group box 1 (HMGB1)-mediated responses to inflammation. Here, we found trimebutine to be a 3D pharmacophore mimetics of papaverine. Trimebutine was revealed to have more potent suppressive effects on HMGB1-induced production of pro-inflammatory cytokines, such as interleukin-6 and tumor necrosis factor-α in macrophage-like RAW264.7 cells and mouse bone marrow primarily differentiated macrophages than did papaverine. However, the inhibitory effect of trimebutine on the interaction of HMGB1 and RAGE was weaker than that of papaverine. Importantly, mechanism-of-action analyses revealed that trimebutine strongly inhibited the activation of RAGE downstream inflammatory signaling pathways, especially the activation of extracellular signal-regulated kinase 1 and 2 (ERK1/2), which are mediator/effector kinases recruited to the intracellular domain of RAGE. Consequently, the activation of Jun amino terminal kinase, which is an important effector kinase for the up-regulation of pro-inflammatory cytokines, was inhibited. Taken together, these results suggest that trimebutine may exert its suppressive effect on the HMGB1-RAGE inflammatory signal pathways by strongly blocking the recruitment of ERK1/2 to the intracellular tail domain of RAGE in addition to its weak inhibition of the extracellular interaction of HMGB1 with RAGE. Thus, trimebutine may provide a unique scaffold for the development of novel dual inhibitors of RAGE for inflammatory diseases.

Structural insights into the active site of poly(ADP-ribose) glycohydrolase using docking modes of 6-hydroxy-3H-xanthen-3-one derivative inhibitors.

Poly(ADP-ribose) glycohydrolase (PARG) plays an essential role in poly(ADP-ribose) (PAR) turnover, and thereby regulating DNA transactions, such as DNA repair, replication, transcription and recombination. Here, we examined the inhibitory activities of 6-hydroxy-3H-xanthene-3-one (HXO) derivatives and analyzed their binding modes in the active site of PARG by in silico docking study. Among the derivatives, Rose Bengal was found to be the most potent inhibitor of PARG and its halogen groups were revealed to cooperatively potentiate the inhibitory activity. Importantly, the binding mode of Rose Bengal occupied the active site of PARG revealed the presence of unique "Sandwich" residues of Asn869 and Tyr792, which enable the inhibitor to bind tightly with the active pocket. This sandwich interaction could stabilize the π-π interactions of HXO scaffold with Phe902 and Tyr795. In addition, to increase the binding affinity, the iodine and chlorine atoms of this inhibitor could contribute to the inducing of favorable disorders, which promote an entropy boost on the active site of PARG for structural plasticity, and making the stable configuration of HXO scaffold in the active site, respectively, as judged by the analysis of binding free energy. These results provide new insights into the active site of PARG and an additional opportunity for designing selective PARG inhibitors.

Strategies for Design of Molecular Structures with a Desired Pharmacophore Using Deep Reinforcement Learning.

The goal of drug design is to discover molecular structures that have suitable pharmacological properties in vast chemical space. In recent years, the use of deep generative models (DGMs) is getting a lot of attention as an effective method of generating new molecules with desired properties. However, most of the properties do not have three-dimensional (3D) information, such as shape and pharmacophore. In drug discovery, pharmacophores are valuable clues in finding active compounds. In this study, we propose a computational strategy based on deep reinforcement learning for generating molecular structures with a desired pharmacophore. In addition, to extract selective molecules against a target protein, chemical genomics-based virtual screening (CGBVS) is used as post-processing method of deep reinforcement learning. As an example study, we have employed this strategy to generate molecular structures of selective TIE2 inhibitors. This strategy can be adopted into general use for generating selective molecules with a desired pharmacophore.

Structural Basis of Beneficial Design for Effective Nicotinamide Phosphoribosyltransferase Inhibitors.

Inhibition of nicotinamide phosphoribosyltransferase (NAMPT) is an attractive therapeutic strategy for targeting cancer metabolism. So far, many potent NAMPT inhibitors have been developed and shown to bind to two unique tunnel-shaped cavities existing adjacent to each active site of a NAMPT homodimer. However, cytotoxicities and resistances to NAMPT inhibitors have become apparent. Therefore, there remains an urgent need to develop effective and safe NAMPT inhibitors. Thus, we designed and synthesized two close structural analogues of NAMPT inhibitors, azaindole-piperidine (3a)- and azaindole-piperazine (3b)-motif compounds, which were modified from the well-known NAMPT inhibitor FK866 (1). Notably, 3a displayed considerably stronger enzyme inhibitory activity and cellular potency than did 3b and 1. The main reason for this phenomenon was revealed to be due to apparent electronic repulsion between the replaced nitrogen atom (N1) of piperazine in 3b and the Nδ atom of His191 in NAMPT by our in silico binding mode analyses. Indeed, 3b had a lower binding affinity score than did 3a and 1, although these inhibitors took similar stable chair conformations in the tunnel region. Taken together, these observations indicate that the electrostatic enthalpy potential rather than entropy effects inside the tunnel cavity has a significant impact on the different binding affinity of 3a from that of 3b in the disparate enzymatic and cellular potencies. Thus, it is better to avoid or minimize interactions with His191 in designing further effective NAMPT inhibitors.

Papaverine identified as an inhibitor of high mobility group box 1/receptor for advanced glycation end-products interaction suppresses high mobility group box 1-mediated inflammatory responses.

The interaction of high mobility group box 1 (HMGB1), which is secreted from immune and dying cells during cellular infection and injury, and receptor for advanced glycation end-products (RAGE) appears to be critical for acute and chronic inflammatory disorders. Here we designed a unique cyclic ß-hairpin peptide (Pepb2), which mimics the predicted RAGE-binding domain of HMGB1. Pepb2 competitively inhibited HMGB1/RAGE interaction. We then identified papaverine as a Pepb2 mimetic by in silico 3D-structural similarity screening from the DrugBank library. Papaverine was found to directly inhibit HMGB1/RAGE interaction. It also suppressed the HMGB1-mediated production of pro-inflammatory cytokines, IL-6 and TNF-α, in mouse macrophage-like RAW264.7 cells and bone marrow-derived macrophages. In addition, papaverine attenuated mortality in cecal ligation puncture-induced sepsis model mice. Taken together, these findings indicate that papaverine could become a useful therapeutic against HMGB1/RAGE-mediated sepsis and other inflammatory diseases.

Method for Systematic Analogue Search Using the Mega SAR Matrix Database.

Analogue searching is a typical requirement in hit expansion, hit-to-lead, and lead optimization projects. A new computational methodology is introduced to search for existing and virtual analogues of active compounds. The approach is based upon the SAR matrix (SARM) data structure that was originally developed for the systematic identification and structural organization of analogue series. The SARM-based analogue search algorithm further extends the capacity of current substructure-based methods by (i) simultaneously considering existing and virtual analogues that populate chemical space around query compounds, (ii) permitting not only R-group replacements but also well-defined chemical modifications in core structures to further expand the analogue space, and (iii) automatically extracting all possible analogues from large pools. In addition, as a basis for analogue searching following the SARM concept, the Mega-SARM database is introduced. Mega-SARM is derived from nearly 3.7 million compounds and contains ∼250 000 matrices with structurally related analogue series and more than 1.5 million virtual candidate compounds.