PADFrag: A Database Built for the Exploration of Bioactive Fragment Space for Drug Discovery

Synthesis and biological evaluation of O4'-benzyl-hispidol derivatives and analogs as dual monoamine oxidase-B inhibitors and anti-neuroinflammatory agents

Design, synthesis, and biological evaluation of two series of O4'-benzyl-hispidol derivatives and the analogous corresponding O3'-benzyl derivatives aiming to develop selective monoamine oxidase-B inhibitors endowed with anti-neuroinflammatory activity is reported herein. The first O4'-benzyl-hispidol derivatives series afforded several more potentially active and MAO-B inhibitors than the O3'-benzyl derivatives series. The most potential compound 2e of O4'-benzyl derivatives elicited sub-micromolar MAO-B IC50 of 0.38 µM with a selectivity index >264 whereas most potential compound 3b of O3'-benzyl derivatives showed only 0.95 MAO-B IC50 and a selectivity index >105. Advancement of the most active compounds showing sub-micromolar activities to further cellular evaluations of viability and induced production of pro-neuroinflammatory mediators confirmed compound 2e as a potential lead compound inhibiting the production of the neuroinflammatory mediator nitric oxide significantly by microglial BV2 cells at 3 µM concentration without significant cytotoxicity up to 30 µM. In silico molecular docking study predicted plausible binding modes with MAO enzymes and provided insights at the molecular level. Overall, this report presents compound 2e as a potential lead compound to develop potential multifunctional compounds.

Applying molecular docking to pesticides

Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Open access databases available for the pesticide lead discovery

Pesticide research is a multi-disciplinary collaborative study, and big data analysis based on integrating information from databases benefits decision-making in pesticide research. In the last 40 years, dozens of pesticide-related databases have been built up to describe their biological activities, toxicity, modes of action, and environmental risks, etc. However, these data are scattered and overlapping in different databases in multiple inconsistent formats, which is not convenient for information analysis and comparison. In this study, the content of 26 open access databases related to pesticide research was illustrated according to the information provided for the ligand-based drug design (LBDD) and receptor-based (or structure-based drug design, SBDD), and was summarized into three categories:1) the correspondence between the chemical structures and functional properties (biological activity, resistance, toxicity, environmental adaptation); 2) action mode study (target identification, target structures, and biological pathways); 3) computational servers for pesticide design. To our knowledge, this is the first review about the open access databases for pesticide research. The data classification could facilitate the information accessibility for pesticide research, and speed up the decision-making process in pesticide discovery.

Expedient Discovery for Novel Antifungal Leads Inhibiting Fusarium graminearum: 3-(Phenylamino)quinazolin-4(3H)-ones Deriving from Systematic Optimizations on a Tryptanthrin Structure

The ever-increasing resistance of Fusarium graminearum has emerged as a pressing agricultural issue that could be settled by developing novel fungicides owning inimitable action mechanisms. With the aim of discovering novel antifungal leads inhibiting F. graminearum, a tryptanthrin structure was dexterously optimized to generate 30 novel quinazolin-4(3H)-one derivatives. The aforementioned optimization generated the molecule C₁₇ that owned exhilarating in vitro anti-F. graminearum effect (EC₅₀ value = 0.76 μg/mL). Whereafter, the in vivo anti-F. graminearum preventative efficacy of the molecule C₁₇ was measured to be 59.5% at 200 μg/mL, which was approximately comparable with that of carbendazim (64.9%). Furthermore, morphological observations indicated that the molecule C₁₇ could cause the hypha to become slender and dense, distort the outline of cell walls, induce an increase in liposome numbers, and cause the reduction of mitochondria numbers. The above results have emerged as an obbligato complement for developing novel antifungal leads that could effectively control Fusarium head blight.

Progress and Impact of Latin American Natural Product Databases

Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element of CADD. This work reviews the progress toward developing compound databases of natural origin, and it surveys computational methods, emphasizing chemoinformatic approaches to profile natural product databases. Furthermore, it reviews the present state of the art in developing Latin American NP databases and their practical applications to the drug discovery area.

Distribution- and Metabolism-Based Drug Discovery: A Potassium-Competitive Acid Blocker as a Proof of Concept

Conventional methods of drug design require compromise in the form of side effects to achieve sufficient efficacy because targeting drugs to specific organs remains challenging. Thus, new strategies to design organ-specific drugs that induce little toxicity are needed. Based on characteristic tissue niche-mediated drug distribution (TNMDD) and patterns of drug metabolism into specific intermediates, we propose a strategy of distribution- and metabolism-based drug design (DMBDD); through a physicochemical property-driven distribution optimization cooperated with a well-designed metabolism pathway, SH-337, a candidate potassium-competitive acid blocker (P-CAB), was designed. SH-337 showed specific distribution in the stomach in the long term and was rapidly cleared from the systemic compartment. Therefore, SH-337 exerted a comparable pharmacological effect but a 3.3-fold higher no observed adverse effect level (NOAEL) compared with FDA-approved vonoprazan. This study contributes a proof-of-concept demonstration of DMBDD and provides a new perspective for the development of highly efficient, organ-specific drugs with low toxicity.

Fragment-based inhibitor design for SARS-CoV2 main protease

COVID-19 disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) has resulted in tremendous loss of lives across the world and is continuing to do so. Extensive work is under progress to develop inhibitors which can prevent the disease by arresting the virus in its life cycle. One such way is by targeting the main protease of the virus which is crucial for the cleavage and conversion of polyproteins into functional units of polypeptides. In this endeavor, our effort was to identify hit molecule inhibitors for SARS-CoV2 main protease using fragment-based drug discovery (FBDD), based on the available crystal structure of chromene-based inhibitor (PDB_ID: 6M2N). The designed molecules were validated by molecular docking and molecular dynamics simulations. The stability of the complexes was further assessed by calculating their binding free energies, normal mode analysis, mechanical stiffness, and principal component analysis.

Real-Time Fluorescence Imaging of the Abscisic Acid Receptor Allows Nondestructive Visualization of Plant Stress

Environmental stress greatly decreases crop yield. The application of noninvasive techniques is one of the most practical and feasible ways of monitoring the health condition of plants under stress. However, it remains largely unsolved. A chemical fluorescent probe can be applied as a typical nondestructive method, but it has not been applied in living plants for stress detection to date. The abscisic acid (ABA) receptor plays a central role in conferring tolerance to environmental stresses and is an excellent target for developing fluorescent probes. Herein, we developed a fluorescence molecular imaging technology to monitor live plant stress by visualizing the protein expression level of the ABA receptor PYR1. A computer-aided designed indicator dye, flubactin, exhibited an 8-fold enhancement in fluorescence intensity upon interaction with PYR1. In vitro and in vivo experiments showed that flubactin is suitable to be used to detect salt stress in plants in real time. Moreover, the low toxicity of flubactin promotes its application in the future. Our work opens a new era for the nondestructive visualization of plant stress in vivo.

Pharmacophore-Oriented Discovery of Novel 1,2,3-Benzotriazine-4-one Derivatives as Potent 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a functional protein existing in almost all aerobic organisms. In the field of agricultural chemicals, HPPD is acknowledged to be one of the crucial targets for herbicides at present due to its unique bio-function in plants. In the Auto Core Fragment in silico Screening (ACFIS) web server, a potential HPPD inhibitor featuring 1,2,3-benzotriazine-4-one was screened out via a pharmacophore-linked fragment virtual screening (PFVS) method. Molecular simulation studies drove the process of "hit-to-lead" optimization, and a family of 1,2,3-benzotriazine-4-one derivatives was synthesized. Consequently, 6-(2-hydroxy-6-oxocyclohex-1-ene-1-carbonyl)-5-methyl-3-(2-methylbenzyl)benzo[d][1,2,3]triazin-4(3H)-one (15bu) was identified to be the best HPPD inhibitor (IC₅₀ = 36 nM) among the 1,2,3-benzotriazine-4-one derivatives, which had over 8-fold improvement of enzyme inhibition compared with the positive control mesotrione (IC₅₀ = 289 nM). Crystallography information for the AtHPPD-15bu complex revealed several important interactions of the ligand bound upon the target protein, i.e., the bidentate chelating interaction of the triketone motif with the metal ion of AtHPPD, a tight π-π stacking interaction consisting of the1,2,3-benzotriazine-4-one moiety and two benzene rings of Phe-424 and Phe-381, and the polydirectional hydrophobic contacts consisting of the ortho-CH₃-benzyl group of the core scaffold and some hydrophobic residues. Furthermore, compound 15bu displayed 100% inhibition against the five species of target weeds at the tested dosage, which was comparable to the weed control of mesotrione. Collectively, the fused 1,2,3-benzotriazine-4-one-triketone hybrid is a promising chemical tool for the development of more potent HPPD inhibitors and provides a valuable lead compound 15bu for herbicide innovation.

Pesticide Informatics Platform (PIP): An International Platform for Pesticide Discovery, Residue, and Risk Evaluation

Pesticides are widely used agrochemicals for crop protection. The need for novel pesticides becomes urgent as a result of the emergence of resistance and environmental toxicity. Pesticide informatics has been applied in different phase processes of pesticide target identification, active ingredient design, and impact evaluation. However, these valuable resources are scattered over the literature and web, limiting their availability. Here, we summarize and connect research on pesticide informatics resources. A pesticide informatics platform (PIP) was constructed to share these tools. We finally discuss the future direction of pesticide informatics, including pesticide contamination. We expect to share the pesticide informatics approaches and stimulate further research.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

Discovery of Novel Cytochrome bc1 Complex Inhibitor Based on Natural Product Neopeltolide

Background:

Natural products (NPs) are important sources for the design of new drugs and agrochemicals. Neopeltolide, a marine NP, has been identified as a potent Qo-site inhibitor of cytochrome bc1 complex.

Methods:

In this study, a series of neopeltolide derivatives was designed and synthesized by the simplification of its 14-membered macrolactone ring with a diphenyl ether fragment. The enzymatic inhibition bioassays and mycelium growth inhibition experiments against a range of fungi were performed to determine their fungicidal activities.

Results:

The derivatives have potent activity against the porcine bc1 complex. Compound 8q showed the best activity with an IC50 value of 24.41 nM, which was 8-fold more effective than that of positive control azoxystrobin. Compound 8a exhibited a 100% inhibitory rate against Zymoseptoria tritici and Alternaria solani at a 20 mg/L dose.

Conclusion:

Computational results indicated that compounds with suitable physicochemical properties, as well as those forming a hydrogen bond with His161, would have good fungicidal activity. These data could be useful for the design of bc1 complex inhibitors in the future.

Evaluation of anti-cancer and anti-covid-19 properties of cationic pentapeptide Glu-Gln-Arg-Pro-Arg, from rice bran protein and its d-isomer analogs through molecular docking simulations

Bioactive peptides derived from food proteins are becoming increasingly popular due to the growing awareness of their health-promoting properties. The structure and mechanism of anti-cancer action of pentapeptide Glu-Gln-Arg-Pro-Arg (EQRPR) derived from a rice bran protein are not known. Theoretical and experimental methods were employed to fill this gap. The conformation analysis of the EQRPR pentapeptide was performed first and the obtained lowest energy conformer was optimized. The experimental structural data obtained by FTIR and CD spectroscopies agree well with the theoretical results. d-isomer introduced one-by-one to each position and all D-isomers of the peptide were also examined for its possible anti-proteolytic and activity enhancement properties. The molecular docking revealed avid binding of the pentapeptide to the integrins α5β1 and αIIbβ3, with Kd values of 90 nM and 180 nM, respectively. Moreover, the EQRPR and its D-isomers showed strong binding affinities to apo- and holo-forms of Mpro, spike glycoprotein, ACE2, and dACE2. The predicted results indicate that the pentapeptide may significantly inhibit SARS-CoV-2 infection. Thus, the peptide has the potential to be the leading molecule in the drug discovery process as having multifunctional with diverse biological activities.

Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities

New chemical entities (NCEs) such as small molecules and antibody-drug conjugates have strong binding affinity for biological targets, which provide deep insights into structure-specific interactions for the design of future drugs. As structures of drugs increase in complexity, the importance of computational predictions comes into sharp focus. Knowledge of various computational tools enables us to predict the molecular properties, toxicity, and biological efficacy of the drugs and help the medicinal chemists to discover new drugs more efficiently. Newly approved drugs have higher affinities for proteins and nucleic acids and are applied for the treatment of human diseases. We have carried out the computational studies of 21 such NCEs, specifically small molecules and antibody-drug conjugates, and studied the biological efficacy of these drugs. Their bioactivity score and molecular and pharmacokinetic properties were evaluated using online computer software programs, viz., Molinspiration and Osiris Property Explorer. The SwissTargetPrediction tool was used for the efficient prediction of protein targets for the NCEs. The results indicated higher stability for the drug complexes due to a larger HOMO-LUMO gap. A high electrophilicity index reflects good electrophilic behavior and high reactivity of the drugs. Lipinski's ''rule of five'' indicated that most of the drug complexes are likely to be orally active. These drugs also showed non-mutagenic, non-tumorigenic, non-irritant, and non-effective reproductive behavior. We hope that these studies will provide an insight into molecular recognition and definitely help the medicinal chemists to design new drugs in future.

Synthesis and Herbicidal Activity of Triketone-Aminopyridines as Potent p-Hydroxyphenylpyruvate Dioxygenase Inhibitors

Exploring novel p-hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) inhibitors has become one of the most promising research directions in herbicide innovation. On the basis of our tremendous interest in exploiting more powerful HPPD inhibitors, we designed a family of benzyl-containing triketone-aminopyridines via a structure-based drug design (SBDD) strategy and then synthesized them. Among these prepared derivatives, the best active 3-hydroxy-2-(3,5,6-trichloro-4-((4-isopropylbenzyl)amino)picolinoyl)cyclohex-2-en-1-one (23, IC₅₀ = 0.047 μM) exhibited a 5.8-fold enhancement in inhibiting Arabidopsis thaliana (At) HPPD activity over that of commercial mesotrione (IC₅₀ = 0.273 μM). The predicted docking models and calculated energy contributions of the key residues for small molecules suggested that an additional π-π stacking interaction with Phe-392 and hydrophobic contacts with Met-335 and Pro-384 were detected in AtHPPD upon the binding of the best active compound 23 compared with that of the reference mesotrione. Such a molecular mechanism and the resulting binding affinities coincide with the proposed design scheme and experimental values. It is noteworthy that inhibitors 16 (3-hydroxy-2-(3,5,6-trichloro-4-((4-chlorobenzyl)amino)picolinoyl)cyclohex-2-en-1-one), 22 (3-hydroxy-2-(3,5,6-trichloro-4-((4-methylbenzyl)amino)picolinoyl)cyclohex-2-en-1-one), and 23 displayed excellent greenhouse herbicidal effects at 150 g of active ingredient (ai)/ha after postemergence treatment. Furthermore, compound 16 showed superior weed-controlling efficacy against Setaria viridis (S. viridis) versus that of the positive control mesotrione at multiple test dosages (120, 60, and 30 g ai/ha). These findings imply that compound 16, as a novel lead of HPPD inhibitors, possesses great potential for application in specifically combating the malignant weed S. viridis.

Fragment-based drug design facilitates selective kinase inhibitor discovery

Protein kinases (PKs) are important drug targets, but kinases selectivity poses a challenge to protein kinase inhibitors (PKIs) design. Fragment-based drug discovery (FBDD) has achieved great success in the discovery of highly specific PKIs. It makes full use of kinase-fragment interaction in target kinase subpockets to obtain promising selectivity. However, it's difficult to understand the complicated kinase-fragment interaction space, and systemic discussion of these interactions is still lacking. Herein, we introduce the advantages of the FBDD strategy in PKIs design. Key features of the selectivity of kinase-fragment interactions are summarized and analyzed. Some promising PKIs are introduced as case studies to help understand the fragment-to-lead (F2L) optimization process. Novel strategies and technologies for FBDD in PKIs discovery are also outlooked.

HerbiPAD: a free web platform to comprehensively analyze constitutive property and herbicide-likeness to estimate chemical bioavailability

BACKGROUND

Herbicides, as efficient weed control measures, play a crucial role in ensuring food security. The emergence of herbicide-resistant weeds has negatively affected food security and promoted the demand for new and improved herbicides. The balance between bioavailability and the potency of a compound is one of the most pressing challenges in the development of novel ideal herbicides. Herbicide-likeness analysis is crucial for the evaluation of this balance and thus may help to address this issue. Many herbicide-likeness analysis methods have been developed to screen potential novel lead compounds. However, there remains a lack of user-friendly and integrated tools to comprehensively evaluate herbicide-likeness.

RESULTS

Herbicide-likeness of compounds was assessed through integrated analysis incorporating the physicochemical properties of commercial herbicides, a qualitative rule, and three quantitative scoring functions developed for evaluating herbicide-likeness. HerbiPAD (http://agroda.gzu.edu.cn:9999/ccb/database/HerbiPAD/) is a free web platform integrated with the collected database and scoring model. This platform contains 542 approved herbicides and > 29 000 physicochemical descriptors. The accuracy of HerbiPAD in distinguishing known herbicides from nonherbicides was 84.2%. In the case study, HerbiPAD evaluated 60 new compounds from seven different herbicide targets, and the accuracy of predicting better bioavailability was 83.3%.

CONCLUSIONS

HerbiPAD was designed to quickly and efficiently evaluate herbicide-likeness by integrating qualitative and quantitative analyses. The simple and effective interpretation of the analysis interface may help noncomputational experts understand herbicide-likeness. © 2020 Society of Chemical Industry.

Protocol for hit-to-lead optimization of compounds by auto in silico ligand directing evolution (AILDE) approach

Hit-to-lead (H2L) optimization is crucial for drug design, which has become an increasing concern in medicinal chemistry. A virtual screening strategy of auto in silico ligand directing evolution (AILDE) has been developed to yield promising lead compounds rapidly and efficiently. The protocol includes instructions for fragment compound library construction, conformational sampling by molecular dynamics simulation, ligand modification by fragment growing, as well as the binding free energy prediction. For complete details on the use and execution of this protocol, please refer to Wu et al. (2020).

FragRep: A Web Server for Structure-Based Drug Design by Fragment Replacement

The design of efficient computational tools for structure-guided ligand design is essential for the drug discovery process. We hereby present FragRep, a new web server for structure-based ligand design by fragment replacement. The input is a protein and a ligand structure, either from protein data bank or from molecular docking. Users can choose specific substructures they want to modify. The server tries to find suitable fragments that not only meet the geometric requirements of the remaining part of the ligand but also fit well with local protein environments. FragRep is a powerful computational tool for the rapid generation of ligand design ideas; either in scaffold hopping or bioisosteric replacing. The FragRep Server is freely available to researchers and can be accessed at http://xundrug.cn/fragrep.

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets

Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the performance characteristics of popular ML algorithms used in QSAR learning is highly desirable. In this study, five linear algorithms [linear function Gaussian process regression (linear-GPR), linear function support vector machine (linear-SVM), partial least squares regression (PLSR), multiple linear regression (MLR) and principal component regression (PCR)], three analogizers [radial basis function support vector machine (rbf-SVM), K-nearest neighbor (KNN) and radial basis function Gaussian process regression (rbf-GPR)], six symbolists [extreme gradient boosting (XGBoost), Cubist, random forest (RF), multiple adaptive regression splines (MARS), gradient boosting machine (GBM), and classification and regression tree (CART)] and two connectionists [principal component analysis artificial neural network (pca-ANN) and deep neural network (DNN)] were employed to learn the regression-based QSAR models for 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. The results show that rbf-SVM, rbf-GPR, XGBoost and DNN generally illustrate better performances than the other algorithms. The overall performances of different algorithms can be ranked from the best to the worst as follows: rbf-SVM > XGBoost > rbf-GPR > Cubist > GBM > DNN > RF > pca-ANN > MARS > linear-GPR ≈ KNN > linear-SVM ≈ PLSR > CART ≈ PCR ≈ MLR. In terms of prediction accuracy and computational efficiency, SVM and XGBoost are recommended to the regression learning for small data sets, and XGBoost is an excellent choice for large data sets. We then investigated the performances of the ensemble models by integrating the predictions of multiple ML algorithms. The results illustrate that the ensembles of two or three algorithms in different categories can indeed improve the predictions of the best individual ML algorithms.

Computational Fragment-Based Design Facilitates Discovery of Potent and Selective Monoamine Oxidase-B (MAO-B) Inhibitor

Parkinson's disease (PD) is one of the most common age-related neurodegenerative diseases. Inhibition of monoamine oxidase-B (MAO-B), which is mainly found in the glial cells of the brain, may lead to an elevated level of dopamine (DA) in patients. MAO-B inhibitors have been used extensively for patients with PD. However, the discovery of the selective MAO-B inhibitor is still a challenge. In this study, a computational strategy was designed for the rapid discovery of selective MAO-B inhibitors. A series of (S)-2-(benzylamino)propanamide derivatives were designed. In vitro biological evaluations revealed that (S)-1-(4-((3-fluorobenzyl)oxy)benzyl)azetidine-2-carboxamide (C3) was more potent and selective than safinamide, a promising drug for regulating MAO-B. Further studies revealed that the selectivity mechanism of C3 was due to the steric clash caused by the residue difference of Phe208 (MAO-A) and Ile199 (MAO-B). Animal studies showed that compound C3 could inhibit cerebral MAO-B activity and alleviate 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopaminergic neuronal loss.

Fragment-based discovery of flexible inhibitor targeting wild-type acetohydroxyacid synthase and P197L mutant

BACKGROUND

Intensifying weed resistance has challenged the use of existing acetohydroxyacid synthase (AHAS)-inhibiting herbicides. Hence, there is currently an urgent requirement for the discovery of a new AHAS inhibitor to effectively control AHAS herbicide-resistant weed species produced by target mutation.

RESULTS

To combat weed resistance caused by AHAS with P197L mutation, we built a structure library consisting of pyrimidinyl-salicylic acid derivatives. Using the pharmacophore-linked fragment virtual screening (PFVS) approach, hit compound 8 bearing 6-phenoxymethyl substituent was identified as a potential AHAS inhibitor with antiresistance effect. Subsequently, derivatives of compound 8 were synthesized and evaluated for their inhibitory activities. The study of the enzyme-based structure-activity relationship and structure-resistance relationship studies led to the discovery of a qualified candidate, 28. This compound not only significantly inhibited the activity of wild-type Arabidopsis thaliana (At) AHAS and P197L mutant, but also exhibited good antiresistance properties (RF = 0.79). Notably, compared with bispyribac at 37.5-150 g of active ingredient per hectare (g a.i. ha^-1 ), compound 27 exhibited higher growth inhibition against both sensitive and resistant Descurainia sophia, CONCLUSION: The title compounds have great potential to be developed as new leads to effectively control herbicide-resistant weeds comprising AHAS with P197L mutation. Also, our study provided a positive case for discovering novel, potent and antiresistance inhibitors using a fragment-based drug design approach.

Design and synthesis of potent inhibitors of bc1 complex based on natural product neopeltolide

Neopeltolide, a natural product isolated from deep-water sponge specimen of the family neopeltidae, has been proven to be a novel inhibitor of cytochrome bc₁. In this study, a series of neopeltolide derivatives was designed by replacing the 14-membered macrolactone with indole ring and confirmed by ¹H NMR, ¹³C NMR, and HRMS. Based on the binding mode of 12h with bc₁ complex, the IC₅₀ values of compounds 16a-f (ranging from 0.70 to 1.46 μM) were improved significantly than the ester derivatives 12a-u by replacing the ester with amide linker. Subsequently, the molecular docking results indicated that compound 16e could form a π-π interaction with Phe274 and two H-bonds with Glu271 and His161 and the latter H-bond was found to account for its high activity. The present work accelerates the discovery of novel bc₁ complex inhibitors to deal with the resistance that the existing bc₁ complex inhibitors are facing and provides a valuable idea for the design of new fungicides.

Auto In Silico Ligand Directing Evolution to Facilitate the Rapid and Efficient Discovery of Drug Lead

Motivated by the growing demand for reducing the chemical optimization burden of H2L, we developed auto in silico ligand directing evolution (AILDE, http://chemyang.ccnu.edu.cn/ccb/server/AILDE), an efficient and general approach for the rapid identification of drug leads in accessible chemical space. This computational strategy relies on minor chemical modifications on the scaffold of a hit compound, and it is primarily intended for identifying new lead compounds with minimal losses or, in some cases, even increases in ligand efficiency. We also described how AILDE greatly reduces the chemical optimization burden in the design of mesenchymal-epithelial transition factor (c-Met) kinase inhibitors. We only synthesized eight compounds and found highly efficient compound 5g, which showed an ∼1,000-fold improvement in in vitro activity compared with the hit compound. 5g also displayed excellent in vivo antitumor efficacy as a drug lead. We believe that AILDE may be applied to a large number of studies for rapid design and identification of drug leads.

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AILDE was developed for the rapid identification of drug leads

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A potent drug lead targeted to c-Met was found by synthesizing only eight compounds

In Silico Design and Analysis of a Kinase-Focused Combinatorial Library Considering Diversity and Quality

A structurally diverse, high-quality, and kinase-focused database plays a critical role in finding hits or leads in kinase drug discovery. Here, we propose a workflow for designing a virtual kinase-focused combinatorial library using existing structures. Based on the analysis of known protein kinase inhibitors (PKIs), detailed fragment optimization, fragment selection, fragment linking, and a molecular filtering scheme were defined. Quick recognition of core fragments that can possibly form dual hydrogen bonds with the hinge region of the ATP-pocket was proposed. Furthermore, three diversity and four quality metrics were chosen for compound library analysis, which can be applied to databases with over 30 million structures. Compared with 13 commercial libraries, our protocol demonstrates a special advantage in terms of good skeleton diversity, acceptable fingerprint diversity, balanced scaffold distribution, and high quality, which can work well not only on existing PKIs, but also on four chosen commercial libraries. Overall, the strategy can greatly facilitate the expansion of a desirable chemical space for kinase drug discovery.

FungiPAD: A Free Web Tool for Compound Property Evaluation and Fungicide-Likeness Analysis

The increasing prevalence of fungal diseases, continual development of resistance, and stringent environmental regulations have revealed an urgent need to develop more selective, safer, resistance-breaking, and cost-effective fungicides. However, most new fungicidal lead compounds fail in their late stages of development as a result of poor solubility or permeability, meaning that they have suboptimal physicochemical properties. Hence, the exploration of advanced technologies for compound "fungicide-likeness" assessment might overcome these obstacles and bring more chemical entities to market. FungiPAD ( http://chemyang.ccnu.edu.cn/ccb/database/FungiPAD/ ) is a free platform employed to predict physicochemical properties, bioavailability, and fungicide-likeness swiftly and powerfully using comprehensive approaches, such as physicochemical radars and qualitative and quantitative analyses. This platform contains data for over 16 000 physicochemical descriptors and the results of 2200 qualitative and 1100 quantitative analyses of marketed fungicides and provides comprehensive fungicide-likeness analysis for different compounds. The user-friendly interface facilitates interpretation and manipulation by non-computational scientists in support of fungicide discovery.