De Novo Design of Multitarget Ligands with an Iterative Fragment-Growing Strategy

Recent Advances in Automated Structure-Based De Novo Drug Design

As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.

Multitargeting HDAC Inhibitors Containing a RAS/RAF Protein Interfering Unit

In this work, a series of multitargeting histone deacetylase (HDAC) inhibitors capable of regulating the signal transduction between RAS protein and downstream effectors were obtained by introducing a zinc-ion-binding group into the framework of rigosertib via different linkers. Among them, two representative compounds, XSJ-7 and XSJ-10, not only showed stronger antiproliferative activity against many types of cancer cells including solid tumor cells but also presented more potent inhibition on different subtypes of HDAC than suberoylanilide hydroxamic acid (SAHA). Significantly, XSJ-10 presented moderate pharmacokinetic behaviors and showed stronger antitumor activity than oxaliplatin, SAHA, and rigosertib in the HT-29 xenograft mouse models without significant systemic toxicity. Research on the anticancer mechanism of XSJ-10 revealed that it can effectively induce the apoptosis of cancer cells and suppress the tumor by strongly inhibiting the RAS-RAF-MEK-ERK signaling pathway and the acetylation level of HDAC3.

Dual Inhibitors of AChE and BACE-1 for Reducing Aβ in Alzheimer's Disease: From In Silico to In Vivo

Alzheimer's disease (AD) is a complex and widespread condition, still not fully understood and with no cure yet. Amyloid beta (Aβ) peptide is suspected to be a major cause of AD, and therefore, simultaneously blocking its formation and aggregation by inhibition of the enzymes BACE-1 (β-secretase) and AChE (acetylcholinesterase) by a single inhibitor may be an effective therapeutic approach, as compared to blocking one of these targets or by combining two drugs, one for each of these targets. We used our ISE algorithm to model each of the AChE peripheral site inhibitors and BACE-1 inhibitors, on the basis of published data, and constructed classification models for each. Subsequently, we screened large molecular databases with both models. Top scored molecules were docked into AChE and BACE-1 crystal structures, and 36 Molecules with the best weighted scores (based on ISE indexes and docking results) were sent for inhibition studies on the two enzymes. Two of them inhibited both AChE (IC₅₀ between 4-7 μM) and BACE-1 (IC₅₀ between 50-65 μM). Two additional molecules inhibited only AChE, and another two molecules inhibited only BACE-1. Preliminary testing of inhibition by F681-0222 (molecule 2) on APPswe/PS1dE9 transgenic mice shows a reduction in brain tissue of soluble Aβ42.

Discovery of spirooxindole-derived small-molecule compounds as novel HDAC/MDM2 dual inhibitors and investigation of their anticancer activity

Simultaneous inhibition of more than one target is considered to be a novel strategy in cancer therapy. Owing to the importance of histone deacetylases (HDACs) and p53-murine double minute 2 (MDM2) interaction in tumor development and their synergistic effects, a series of MDM2/HDAC bifunctional small-molecule inhibitors were rationally designed and synthesized by incorporating an HDAC pharmacophore into spirooxindole skeletons. These compounds exhibited good inhibitory activities against both targets. In particular, compound 11b was demonstrated to be most potent for MDM2 and HDAC, reaching the enzyme inhibition of 68% and 79%, respectively. Compound 11b also showed efficient antiproliferative activity towards MCF-7 cells with better potency than the reference drug SAHA and Nutlin-3. Furthermore, western blot analysis revealed that compound 11b increased the expression of p53 and Ac-H4 in MCF-7 cells in a dose-dependent manner. Our results indicate that dual inhibition of HDAC and MDM2 may provide a novel and efficient strategy for the discovery of antitumor drug in the future.

XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein-Ligand Scoring and Ranking

Prediction of protein-ligand binding affinities is a central issue in structure-based computer-aided drug design. In recent years, much effort has been devoted to the prediction of the binding affinity in protein-ligand complexes using machine learning (ML). Due to the remarkable ability of ML methods in nonlinear fitting, ML-based scoring functions (SFs) can deliver much improved performance on a selected test set, such as the comparative assessment of scoring functions (CASF), when compared to the classical SFs. However, the performance of ML-based SFs heavily relies on the overall similarity of the training set and the test set. To improve the performance and transferability of an SF, we have tried to combine various features including energy terms from X-score and AutoDock Vina, the properties of ligands, and the statistical sequence-related information from either the binding site or the full protein. In conjunction with extreme trees (ET), an ML model, we have developed XLPFE, a new SF. Compared with other tested methods such as X-score, AutoDock Vina, ΔvinaXGB, PSH-ML, or CNN-score, XLPFE achieves consistently better scoring and ranking power for various types of protein-ligand complex structures beyond the CASF, suggesting that XLPFE has superior transferability. In particular, XLPFE performs better with metalloenzymes. With its faster speed, improved accuracy, and better transferability, XLPFE could be usefully applied to a diverse range of protein-ligand complexes.

Multitarget Action of Xanthones from Garcinia mangostana against α-Amylase, α-Glucosidase and Pancreatic Lipase

Digestive enzymes such α-amylase (AA), α-glucosidase (AG) and pancreatic lipase (PL), play an important role in the metabolism of carbohydrates and lipids, being attractive therapeutic targets for the treatment of type 2 diabetes and obesity. Garcinia mangostana is an interesting species because there have been identified xanthones with the potential to inhibit these enzymes. In this study, the multitarget inhibitory potential of xanthones from G. mangostana against AA, AG and PL was assessed. The methodology included the isolation and identification of bioactive xanthones, the synthesis of some derivatives and a molecular docking study. The chemical study allowed the isolation of five xanthones (1–5). Six derivatives (6–11) were synthesized from the major compound, highlighting the proposal of a new solvent-free methodology with microwave irradiation for obtaining aromatic compounds with tetrahydropyran cycle. Compounds with multitarget activity correspond to 2, 4, 5, 6 and 9, highlighting 6 with IC₅₀ values of 33.3 µM on AA, 69.2 µM on AG and 164.4 µM on PL. Enzymatic kinetics and molecular docking studies showed that the bioactive xanthones are mainly competitive inhibitors on AA, mixed inhibitors on AG and non-competitive inhibitors on PL. The molecular coupling study established that the presence of methoxy, hydroxyl and carbonyl groups are important in the activity and interaction of polyfunctional xanthones, highlighting their importance depending on the mode of inhibition.

Evodiamine-Inspired Topoisomerase-Histone Deacetylase Dual Inhibitors: Novel Orally Active Antitumor Agents for Leukemia Therapy

On the basis of the synergism of topoisomerase (Top) and histone deacetylase (HDAC) inhibitors in antitumor therapy, a series of novel Top/HDAC dual inhibitors were designed and synthesized by the pharmacophore fusion strategy. After systematic structure-activity relationship studies, lead compound 16j was identified to simultaneously inhibit both Top and HDAC with good potency, which showed potent antiproliferative activities with a broad spectrum. Mechanistic studies indicated that compound 16j efficiently induced apoptosis with S cell-cycle arrest in HEL cancer cells. It was orally active in HEL xenograft models and exhibited excellent in vivo antitumor efficacy (TGI = 68.5%; 10 mg/kg). Altogether, this work highlights the therapeutic potential of evodiamine-inspired Top/HDAC dual inhibitors and provides a valuable lead compound for the development of novel antitumor agents for leukemia therapy.

Computational Methods for Structure-Based Drug Design Through System Biology

The advances in computational chemistry and biology, computer science, structural biology, and molecular biology go in parallel with the rapid progress in target-based systems. This technique has become a powerful tool in medicinal chemistry for the identification of hit molecules. The recent developments in target-based systems have played a major role in the creation of libraries of compounds, and it has also been widely applied for the design of molecular docking methods. The main advantage of this method is that it hits the fragment that has the strongest binding, has relatively small size, and leads to better compounds in terms of pharmacokinetic properties when compared with virtual screening (VS) and high-throughput screening (HTS) hits. De novo design is an essential aspect of target-based systems and requires the synthesis of chemical to allow the design of promising compound.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Molecular hybrids: A five-year survey on structures of multiple targeted hybrids of protein kinase inhibitors for cancer therapy

Protein kinases have grown over the past few years as a crucial target for different cancer types. With the multifactorial nature of cancer, and the fast development of drug resistance for conventional chemotherapeutics, a strategy for designing multi-target agents was suggested to potentially increase drug efficacy, minimize side effects and retain the proper pharmacokinetic properties. Kinase inhibitors were used extensively in such strategy. Different kinase inhibitor agents which target EGFR, VEGFR, c-Met, CDK, PDK and other targets were merged into hybrids with conventional chemotherapeutics such as tubulin polymerization and topoisomerase inhibitors. Other hybrids were designed gathering kinase inhibitors with targeted cancer therapy such as HDAC, PARP, HSP 90 inhibitors. Nitric oxide donor molecules were also merged with kinase inhibitors for cancer therapy. The current review presents the hybrids designed in the past five years discussing their design principles, results and highlights their future perspectives.

Discovery of a Dual Tubulin and Poly(ADP-Ribose) Polymerase-1 Inhibitor by Structure-Based Pharmacophore Modeling, Virtual Screening, Molecular Docking, and Biological Evaluation

Dual inhibition of tubulin and poly(ADP-ribose) polymerase-1 (PARP-1) may become an attractive approach for cancer therapy. Here, we discover a dual tubulin/PARP-1 inhibitor (termed as TP-3) using structure-based virtual screening. TP-3 shows strong dual inhibitory effects on both tubulin and PARP-1. Cellular assays reveal that TP-3 shows superior antiproliferative activities against human cancer cells, including breast, liver, ovarian, and cervical cancers. Further studies indicate that TP-3 plays an antitumor role through multiple mechanisms, including the disturbance of the microtubule network and the PARP-1 DNA repairing function, accumulation of DNA double-strand breaks, inhibition of the tube formation, and induction of G2/M cell cycle arrest and apoptosis. In vivo assessment indicates that TP-3 inhibits the growth of MDA-MB-231 xenograft tumors in nude mouse with no notable side effects. These data demonstrate that TP-3 is a dual-targeting, high-efficacy, and low-toxic antitumor agent.

Structure-based de novo drug design using 3D deep generative models

Deep generative models are attracting much attention in the field of de novo molecule design. Compared to traditional methods, deep generative models can be trained in a fully data-driven way with little requirement for expert knowledge. Although many models have been developed to generate 1D and 2D molecular structures, 3D molecule generation is less explored, and the direct design of drug-like molecules inside target binding sites remains challenging. In this work, we introduce DeepLigBuilder, a novel deep learning-based method for de novo drug design that generates 3D molecular structures in the binding sites of target proteins. We first developed Ligand Neural Network (L-Net), a novel graph generative model for the end-to-end design of chemically and conformationally valid 3D molecules with high drug-likeness. Then, we combined L-Net with Monte Carlo tree search to perform structure-based de novo drug design tasks. In the case study of inhibitor design for the main protease of SARS-CoV-2, DeepLigBuilder suggested a list of drug-like compounds with novel chemical structures, high predicted affinity, and similar binding features to those of known inhibitors. The current version of L-Net was trained on drug-like compounds from ChEMBL, which could be easily extended to other molecular datasets with desired properties based on users' demands and applied in functional molecule generation. Merging deep generative models with atomic-level interaction evaluation, DeepLigBuilder provides a state-of-the-art model for structure-based de novo drug design and lead optimization.

DeepLigBuilder, a novel deep generative model for structure-based de novo drug design, directly generates 3D structures of drug-like compounds in the target binding site.

A Novel Benzopyrane Derivative Targeting Cancer Cell Metabolic and Survival Pathways

(1) Background: Today, the discovery of novel anticancer agents with multitarget effects and high safety margins represents a high challenge. Drug discovery efforts indicated that benzopyrane scaffolds possess a wide range of pharmacological activities. This spurs on building a skeletally diverse library of benzopyranes to identify an anticancer lead drug candidate. Here, we aim to characterize the anticancer effect of a novel benzopyrane derivative, aiming to develop a promising clinical anticancer candidate. (2) Methods: The anticancer effect of SIMR1281 against a panel of cancer cell lines was tested. In vitro assays were performed to determine the effect of SIMR1281 on GSHR, TrxR, mitochondrial metabolism, DNA damage, cell cycle progression, and the induction of apoptosis. Additionally, SIMR1281 was evaluated in vivo for its safety and in a xenograft mice model. (3) Results: SIMR1281 strongly inhibits GSHR while it moderately inhibits TrxR and modulates the mitochondrial metabolism. SIMR1281 inhibits the cell proliferation of various cancers. The antiproliferative activity of SIMR1281 was mediated through the induction of DNA damage, perturbations in the cell cycle, and the inactivation of Ras/ERK and PI3K/Akt pathways. Furthermore, SIMR1281 induced apoptosis and attenuated cell survival machinery. In addition, SIMR1281 reduced the tumor volume in a xenograft model while maintaining a high in vivo safety profile at a high dose. (4) Conclusions: Our findings demonstrate the anticancer multitarget effect of SIMR1281, including the dual inhibition of glutathione and thioredoxin reductases. These findings support the development of SIMR1281 in preclinical and clinical settings, as it represents a potential lead compound for the treatment of cancer.

Fine-tuning of a generative neural network for designing multi-target compounds

Exploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.

Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure-activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.

Multiple Target Drug Design Using LigBuilder 3

Designing drugs that directly interact with multiple targets is a promising approach for treating complicated diseases. In order to successfully bind to multiple targets of different families and achieve the desired ligand efficiency, multi-target-directed ligands (MTDLs) require a higher level of diversity and complexity. De novo design strategies for creating more diverse chemical entities with desired properties may present an improved approach for developing MTDLs. In this chapter, we describe a computational protocol for developing MTDLs using the first reported multi-target de novo program, LigBuilder 3, which combines a binding site prediction module with de novo drug design and optimization modules. As an illustration of each detailed procedure, we design dual-functional compounds of two well-characterized virus enzymes, HIV protease and reverse transcriptase (PR and RT, respectively), using fragments extracted from known inhibitors. LigBuilder 3 is accessible at http://www.pkumdl.cn/ligbuilder3/ .

Design, synthesis and biological evaluation of sulfamoylphenyl-quinazoline derivatives as potential EGFR/CAIX dual inhibitors

Multi-target, especially dual-target, drug design has become a popular research field for cancer treatment. Development of small molecule dual-target inhibitors through hybridization strategy can provide highly potent and selective anticancer agents. In this study, three series of quinazoline derivatives bearing a benzene-sulfonamide moiety were designed and synthesized as dual EGFR/CAIX inhibitors. All the synthesized compounds were evaluated against epidermoid carcinoma (A431) and non-small cell lung cancer (A549 and H1975) cell lines, which displayed weak to potent anticancer activity. In particular, compound 8v emerged as the most potent derivative against mutant-type H1975 cells, which exhibited comparable activity to osimertinib. Importantly, 8v exhibited stronger anti-proliferative activity than osimertinib against H1975 cells under hypoxic condition. Kinase inhibition studies indicated that 8v showed excellent inhibitory effect on EGFR^T790M enzyme, which was 41 times more effective than gefitinib and almost equal to osimertinib. Mechanism studies revealed that 8v exhibited remarkable CAIX inhibitory effect comparable to acetazolamide and significantly inhibited the expression of p-EGFR as well as its downstream p-AKT and p-ERK in H1975 cells. Notably, 8v was found to inhibit the expression of CAIX and its upstream HIF-1α in H1975 cells under hypoxic condition. Molecular docking was also performed to gain insights into the ligand-binding interactions of 8v inside EGFR^WT, EGFR^T790M and CAIX binding sites.

Comprehensive review for anticancer hybridized multitargeting HDAC inhibitors

Despite the encouraging clinical progress of chemotherapeutic agents in cancer treatment, innovation and development of new effective anticancer candidates still represents a challenging endeavor. With 15 million death every year in 2030 according to the estimates, cancer has increased rising of an alarm as a real crisis for public health and health systems worldwide. Therefore, scientist began to introduce innovative solutions to control the cancer global health problem. One of the promising strategies in this issue is the multitarget or smart hybrids having two or more pharmacophores targeting cancer. These rationalized hybrid molecules have gained great interests in cancer treatment as they are capable to simultaneously inhibit more than cancer pathway or target without drug-drug interactions and with less side effects. A prime important example of these hybrids, the HDAC hybrid inhibitors or referred as multitargeting HDAC inhibitors. The ability of HDAC inhibitors to synergistically improve the efficacy of other anti-cancer drugs and moreover, the ease of HDAC inhibitors cap group modification prompt many medicinal chemists to innovate and develop new generation of HDAC hybrid inhibitors. Notably, and during this short period, there are four HDAC inhibitor hybrids have entered different phases of clinical trials for treatment of different types of blood and solid tumors, namely; CUDC-101, CUDC-907, Tinostamustine, and Domatinostat. This review shed light on the most recent hybrids of HDACIs with one or more other cancer target pharmacophore. The designed multitarget hybrids include topoisomerase inhibitors, kinase inhibitors, nitric oxide releasers, antiandrogens, FLT3 and JAC-2 inhibitors, PDE5-inhibitors, NAMPT-inhibitors, Protease inhibitors, BRD4-inhibitors and other targets. This review may help researchers in development and discovery of new horizons in cancer treatment.

Multi-targeted drug design strategies for the treatment of schizophrenia

INTRODUCTION

Schizophrenia is a complex psychiatric disease (or a conglomeration of disorders) manifesting with positive, negative and cognitive symptoms. The pathophysiology of schizophrenia is not completely known; however, it involves many neurotransmitters and their receptors. In order to treat schizophrenia, drugs need to be multi-target drugs. Indeed, the action of second and third generation antipsychotics involves interactions with many receptors, belonging mainly to aminergic GPCRs.

AREAS COVERED

In this review, the authors summarize current concepts of schizophrenia with the emphasis on the modern dopaminergic, serotoninergic, and glutamatergic hypotheses. Next, they discuss treatments of the disease, stressing multi-target antipsychotics. They cover different aspects of design of multi-target ligands, including the application of molecular modeling approaches for the design and benefits and limitations of multifunctional compounds. Finally, they present successful case studies of multi-target drug design against schizophrenia.

EXPERT OPINION

Treatment of schizophrenia requires the application of multi-target drugs. While designing single target drugs is relatively easy, designing multifunctional compounds is a challenge due to the necessity to balance the affinity to many targets, while avoiding promiscuity and the problems with drug-likeness. Multi-target drugs bring many benefits: better efficiency, fewer adverse effects, and drug-drug interactions and better patient compliance to drug regime.

Computer-Aided Fragment Growing Strategies to Design Dual Inhibitors of Soluble Epoxide Hydrolase and LTA4 Hydrolase

Multitarget ligands are interesting candidates for drug discovery and development due to improved safety and efficacy. However, rational design and optimization of multitarget ligands is tedious because affinity optimization for two or more targets has to be performed simultaneously. In this study, we demonstrate that, given a molecular fragment, which binds to two targets of interest, computer-aided fragment growing can be applied to optimize compound potency, relying on either ligand- or structure-derived information. This methodology is applied to the design of dual inhibitors of soluble epoxide hydrolase and leukotriene A4 hydrolase.

Design, Synthesis, and Biological Evaluation of Quinazolin-4-one-Based Hydroxamic Acids as Dual PI3K/HDAC Inhibitors

A series of quinazolin-4-one based hydroxamic acids was rationally designed and synthesized as novel dual PI3K/HDAC inhibitors by incorporating an HDAC pharmacophore into a PI3K inhibitor (Idelalisib) via an optimized linker. Several of these dual inhibitors were highly potent (IC₅₀ < 10 nM) and selective against PI3Kγ, δ and HDAC6 enzymes and exhibited good antiproliferative activity against multiple cancer cell lines. The lead compound 48c, induced necrosis in several mutant and FLT3-resistant AML cell lines and primary blasts from AML patients, while showing no cytotoxicity against normal PBMCs, NIH3T3, and HEK293 cells. Target engagement of PI3Kδ and HDAC6 by 48c was demonstrated in MV411 cells using the cellular thermal shift assay (CETSA). Compound 48c showed good pharmacokinetics properties in mice via intraperitoneal (ip) administration and provides a means to examine the biological effects of inhibiting these two important enzymes with a single molecule, either in vitro or in vivo.

LigBuilder V3: A Multi-Target de novo Drug Design Approach

With the rapid development of systems-based pharmacology and poly-pharmacology, method development for rational design of multi-target drugs has becoming urgent. In this paper, we present the first de novo multi-target drug design program LigBuilder V3, which can be used to design ligands to target multiple receptors, multiple binding sites of one receptor, or various conformations of one receptor. LigBuilder V3 is generally applicable in de novo multi-target drug design and optimization, especially for the design of concise ligands for protein targets with large difference in binding sites. To demonstrate the utility of LigBuilder V3, we have used it to design dual-functional inhibitors targeting HIV protease and HIV reverse transcriptase with three different strategy, including multi-target de novo design, multi-target growing, and multi-target linking. The designed compounds were computational validated by MM/GBSA binding free energy estimation as highly potential multi-target inhibitors for both HIV protease and HIV reverse transcriptase. The LigBuilder V3 program can be downloaded at “http://www.pkumdl.cn/ligbuilder3/”.

Development of pyrazole and spiropyrazoline analogs as multifunctional agents for treatment of Alzheimer's disease

Cholinergic hypothesis of Alzheimer's disease has been advocated as an essential tool in the last couple of decades for the drug development. Here in, we report de novo fragment growing strategy for the design of novel 3,5-diarylpyrazoles and hit optimization of spiropyrazoline derivatives as acetyl cholinesterase inhibitors. Both type of scaffolds numbering forty compounds were synthesized and evaluated for their potencies against AChE, BuChE and PAMPA. Introduction of lipophilic cyclohexane ring in 3,5-diarylpyrazole analogs led to spiropyrazoline derivatives, which facilitated and improved the potencies. Compound 44 (AChE = 1.937 ± 0.066 µM; BuChE = 1.166 ± 0.088 µM; hAChE = 1.758 ± 0.095 µM; P_e = 9.491 ± 0.34 × 10^-6 cm s¹) showed positive results, which on further optimization led to the development of compound 67 (AChE = 0.464 ± 0.166 µM; BuChE = 0.754 ± 0.121 µM; hAChE = 0.472 ± 0.042 µM; P_e = 13.92 ± 0.022 × 10^-6 cm s¹). Compounds 44 and 67 produced significant displacement of propidium iodide from the peripheral anionic site (PAS) of AChE. They were found to be safer to MC65 cells and decreased metal induced Aβ_1-42 aggregation. Further, in-vivo behavioral studies, on scopolamine induced amnesia model, the compounds resulted in better percentage spontaneous alternation scores and were safe, had no influence on locomotion in tested animal groups at dose of 3 mg/kg. Early pharmacokinetic assessment of optimized hit molecules was supportive for further drug development.

Automated De Novo Drug Design: Are We Nearly There Yet?

Medicinal chemistry and, in particular, drug design have often been perceived as more of an art than a science. The many unknowns of human disease and the sheer complexity of chemical space render decision making in medicinal chemistry exceptionally demanding. Computational models can assist the medicinal chemist in this endeavour. Provided here is an overview of recent examples of automated de novo molecular design, a discussion of the concepts and computational approaches involved, and the daring prediction of some of the possibilities and limitations of drug design using machine intelligence.

Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds

Multitargeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation, or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multitarget compound development with valuable tools. In this Perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite some characteristic challenges remaining unresolved, designed polypharmacology holds enormous potential to secure future therapeutic innovation.

Identification of Antifungal Targets Based on Computer Modeling

Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host⁻pathogen interaction databases. Further data&mdash;such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes&mdash;refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets

De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.

Demonstration of AutoDock as an Educational Tool for Drug Discovery

Drug design and discovery remains a popular topic of study to many students interested in visible, real-world applications of the chemical sciences. It is important that laboratory experiments detailing the early stages of drug discovery incorporate both compound design and an exploration of ligand/receptor interactions. Molecular modeling is widely employed in research endeavors seeking to predict the activity of potential compounds prior to synthesis and can therefore be used to illustrate these concepts. The following activity therefore details the use of AutoDock to predict the binding affinity and docked pose of a series of CDK2 inhibitors. Students can then compare their docking output to experimentally determined inhibitory activities and crystal structures. Finally, the AutoDock workflow detailed in this activity can be used in research settings, provided the receptor crystal structure is known.

Computational Multitarget Drug Design

Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.

In silico methods to address polypharmacology: current status, applications and future perspectives

Polypharmacology, a new paradigm in drug discovery that focuses on multi-target drugs (MTDs), has potential application for drug repurposing, the process of finding new uses for existing approved drugs, prediction of off-target toxicities and rational design of MTDs. In this scenario, computational strategies have demonstrated great potential in predicting polypharmacology and in facilitating drug repurposing. Here, we provide a comprehensive overview of various computational approaches that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes and outline their potential for drug discovery. We give an outlook on the latest advances in rational design of MTDs and discuss possible future directions of algorithm development in this field.

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

The unbinding studies of vascular endothelial growth factor receptor-2 protein tyrosine kinase type II inhibitors

Vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase has two conformations, active and inactive conformations. Type II inhibitors bind to inactive conformation. It has two possible binding/unbinding paths. To explore the unbinding path of inhibitor 01-435 that was generated by fragment build in the binding pocket of VEGFR-2, molecular dynamics (MD) simulation was performed on the crystal structure of VEGFR-2 in complex with 01-435, then steered molecular dynamics (SMD) simulation was executed on the crystal structure of VEGFR-2 in complex with 01-435. Pull force, van der Waals and electrostatic interaction along the two paths were calculated by using SMD simulation. The SMD simulation results indicate that the more favorable path for inhibitor dissociation is along with the traditional ATP-channel rather than the allosteric-pocket-channel, which is mainly due to the less electrostatic interaction that the ligand suffers during dissociation process along the traditional ATP-channel.

Future De Novo Drug Design

The computer-assisted generation of new chemical entities (NCEs) has matured into solid technology supporting early drug discovery. Both ligand- and receptor-based methods are increasingly used for designing small lead- and druglike molecules with anticipated multi-target activities. Advanced "polypharmacology" prediction tools are essential pillars of these endeavors. In addition, it has been realized that iterative design-synthesis-test cycles facilitate the rapid identification of NCEs with the desired activity profile. Lab-on-a-chip platforms integrating synthesis, analytics and bioactivity determination and controlled by adaptive, chemistry-driven de novo design software will play an important role for future drug discovery.