Compound Databases Research Articles

Identification of new potent NLRP3 inhibitors by multi-level in-silico approaches

Nod-like receptor protein 3 (NLRP-3), is an intracellular sensor that is involved in inflammasome activation, and the aberrant expression of NLRP3 is responsible for diabetes mellitus, its complications, and many other inflammatory diseases. NLRP3 is considered a promising drug target for novel drug design. Here, a pharmacophore model was generated from the most potent inhibitor, and its validation was performed by the Gunner-Henry scoring method. The validated pharmacophore was used to screen selected compounds databases. As a result, 646 compounds were mapped on the pharmacophore model. After applying Lipinski's rule of five, 391 hits were obtained. All the hits were docked into the binding pocket of target protein. Based on docking scores and interactions with binding site residues, six compounds were selected potential hits. To check the stability of these compounds, 100 ns molecular dynamic (MD) simulations were performed. The RMSD, RMSF, DCCM and hydrogen bond analysis showed that all the six compounds formed stable complex with NLRP3. The binding free energy with the MM-PBSA approach suggested that electrostatic force, and van der Waals interactions, played a significant role in the binding pattern of these compounds. Thus, the outcomes of the current study could provide insights into the identification of new potential NLRP3 inflammasome inhibitors against diabetes and its related disorders.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design withmultiple factors orphenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization,and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening

Virtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases. Notably, FaissMolLib employs the highly efficient Faiss search algorithm, outperforming the Tanimoto algorithm in identifying similar molecules with its tighter clustering in scatter plots and lower mean, standard deviation, and variance in key molecular properties. This feature enables FaissMolLib to screen 2.8 million compounds in just 0.05 seconds, offering researchers an efficient, easily deployable solution for virtual screening on laptops and building unique compound databases. This significant advancement holds great potential for accelerating drug discovery efforts and enhancing chemical data analysis. FaissMolLib is freely available at http://liuhaihan.gnway.cc:80. The code and dataset of FaissMolLib are freely available at https://github.com/Superhaihan/FiassMolLib.

IN SILICO SCREENING BY MOLECULAR DOCKING OF HETEROCYCLIC COMPOUNDS WITH FURAN OR INDOLE NUCLEUS FROM DATABASE FOR ANTICANCER ACTIVITY AND VALIDATION OF THE METHOD BY REDOCKING

Objective: This study aims to perform in silico screening of nine heterocyclic ligands containing furan or indole with oxygen in their structure selected from the compound database based on a literature review for predicting their anticancer activity on tyrosine kinase receptor receptors. Methods: The receptor is complex with the ligand Gliteritinib and was downloaded from the protein database. The ligands used for this study were 5-fluoro-1H-indole-2-carboxylic acid,2(5H)-Furanone Furfuryl pentanoate, Furan-2,5-dicarbaldehyde, 2,5-Furandicarboxylic acid, Furan-2-yl(1H-indol-3-yl) methanone, Tert-butyl 3-formyl-1H-indole-1-carboxylate,7-Amino-5-fluoroindolin-2-one,7H-Furo[3,2-g]chromen-7-one. Pyrex molecular docking software was used to perform the analysis. The study was validated using a re-docking technique using the ligand Gliteritinib. Results: A good docking score of (-7.8) was obtained for tert-butyl 3-formyl-1H-indole-1-carboxylate, leading to promising activity prediction. Furan-2-yl(1H-indol-3-yl) methadone and 7H-Furo[3,2-g]chromen-7-one also scored well with (-7.5) and (-7.3) respectively. The redocking process resulted in a score of (-9.2). Conclusion: Values are comparable to the root primary square value, showing the reproducibility of this method. The finding gives insight into Insilco docking for anticancer activity and further exploration of phytochemicals for Insilco screening.

Using network pharmacology and molecular docking to uncover the mechanism by which quercetin alleviates deoxynivalenol-induced porcine intestinal injury

Deoxynivalenol is a widespread feed contaminant that leads to vomit, which results in serious symptom such as increased intestinal permeability and even intestinal mucosal necrosis. Recent studies have reported the role of quercetin in alleviating deoxynivalenol-induced intestinal injury; however, the mechanisms and targets remain unclear. Thus, we aimed to identify the mechanisms of action by using a combination of network pharmacology and molecular docking. We identified 151 quercetin targets, 235 deoxynivalenol targets and 47 porcine intestinal injury targets by searching compound database and PubMed database, among which there were two common targets. The PPI network showed that the key proteins involved are NQO1 and PPAR-γ. The PPI network showed that the key proteins involved were NQO1 and PPARG. GO analysis found that genes were enriched primarily in response to oxidative stress. The PPI network showed that the key proteins involved are NQO1 and PPAR-γ. The genes are enriched primarily in response to oxidative stress. KEGG analysis showed enrichment of the HIF, reactive oxygen species and other signaling pathways. The molecular docking results indicated key binding activity between NQO1–quercetin and PPAR-γ–quercetin. By using network pharmacology, we have revealed the potential molecular mechanisms by which quercetin alleviates deoxynivalenol-induced porcine intestinal injury, which lays the foundation for the development of drugs to treat deoxynivalenol-induced intestinal injury in pigs.

Rapid identification and determination of chemical components of huai yam based on UPLC-Q-Exactive-MS and fragmentation patterns

Huai Yam (Dioscoreae Rhizoma) contains many active ingredients such as flavonoids, saponins, and amino acids. In this study, an efficient method for the classification and rapid identification of yam components was established based on UPLC-Q-Exactive-MS and data post-processing techniques. First, the mass spectrometry information including the characteristic fragmentations (CFs) and neutral losses (NLs) of yam reported in the literature were summarised and a database of compounds was established. Then, the mass spectrometry data detected by the yam sample are compared with those described in database for rapid identification of target compounds. Finally, 60 compounds were identified, including 18 flavones, 2 saponins, 10 amino acids, 7 organic acids, 3 carbohydrates, 8 fatty acids and 12 others. A new strategy for identifying target constituents based on CFs and NLs was successfully established, laying the foundation for further research on yam and promoting the development of composition analysis of Traditional Chinese Medicine (TCM).

Establishment of compound database of emerging antioxidants and high-resolution mass spectrometry screening in lake sediment from Taihu Lake Basin, China.

Antioxidants are ubiquitous in various environmental samples, leading to increasing concern regarding their potential risk to environments or humans. However, there is dearth of information regarding the environmental fate of antioxidants and unknown/unexpected antioxidants in the environment. Here, we established a compound database (CDB) containing 320 current-used antioxidants by collecting the chemicals from EPA's functional use database and published documents. Physical-chemical characteristics of these antioxidants were estimated, and 19 ones were considered as persistent and bioaccumulative (P&B) substances. This CDB was further coupled with high resolution mass spectrometry (HRMS) technique, which was employed for suspect screening of antioxidants in extracts of sediments (n = 88) collected from Taihu Lake basin. We screened 119 HRMS features that can match 135 chemical formulas in the CDB, and 20 out of them exhibited the detection frequencies ≥ 90%. The total concentrations of suspect antioxidants in sediments ranged from 6.41 to 830ng/g dw. Statistical analysis demonstrated that concentrations of suspect antioxidants in Taihu Lake were statistically significantly lower than those in Shihu and Jiulihu Lake, but greater than those from other small lakes. Collectively, this study provided a CDB that could be helpful for further monitoring studies of antioxidant in the environments, and also provided the first evidence regarding the ubiquity of antioxidants in aquatic environment of Taihu Lake basin.

HSQC Spectra Simulation and Matching for Molecular Identification.

In the pursuit of improved compound identification and database search tasks, this study explores heteronuclear single quantum coherence (HSQC) spectra simulation and matching methodologies. HSQC spectra serve as unique molecular fingerprints, enabling a valuable balance of data collection time and information richness. We conducted a comprehensive evaluation of the following four HSQC simulation techniques: ACD/Labs (ACD), MestReNova (MNova), Gaussian NMR calculations (DFT), and a graph-based neural network (ML). For the latter two techniques, we developed a reconstruction logic to combine proton and carbon 1D spectra into HSQC spectra. The methodology involved the implementation of three peak-matching strategies (minimum-sum, Euclidean-distance, and Hungarian distance) combined with three padding strategies (zero-padding, peak-truncated, and nearest-neighbor double assignment). We found that coupling these strategies with a robust simulation technique facilitates the accurate identification of correct molecules from similar analogues (regio- and stereoisomers) and allows for fast and accurate large database searches. Furthermore, we demonstrated the efficacy of the best-performing methodology by rectifying the structures of a set of previously misidentified molecules. This research indicates that effective HSQC spectral simulation and matching methodologies significantly facilitate molecular structure elucidation. Furthermore, we offer a Google Colab notebook for researchers to use our methods on their own data (https://github.com/AstraZeneca/hsqc_structure_elucidation.git).

Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors

The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells to learn the properties of drug compounds in the DrugBank, aiming to obtain a new and virtual screening compounds database with drug-like properties. Ultimately, a compounds database consisting of 26,316 compounds is obtained by this method. To evaluate the potential of this compounds database, a series of tests are performed, including chemical space, ADME properties, compound fragmentation, and synthesizability analysis. As a result, it is proved that the database is equipped with good drug-like properties and a relatively new backbone, its potential in virtual screening is further tested. Finally, a series of seedling compounds with completely new backbones are obtained through docking and binding free energy calculations.

Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence.

Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.

SGPocket: A New Graph Convolutional Neural Network for Ligand-protein Binding Site Prediction.

Drug research is a long process, taking more than 10 years and requiring considerable financial resources. Therefore, researchers and industrials aim to reduce time and cost. Thus, they use computational simulations like molecular docking to explore huge databases of compounds and extract the most promising ones for further tests. Structure-based molecular docking is a complex process mixing surface exploration and energy computation to find the minimal free energy of binding corresponding to the best interaction location. Our work is developed in the ligand-protein context, where ligands are small compounds like drugs. In most cases, no information is known about where on the protein surface the ligand will bind. Thus, the whole protein surface must be explored, which takes a huge amount of time. We have developed SGPocket (meaning Spherical Graph Pocket), a binding site prediction method. Our method allows us to reduce the explored protein surface using deep learning without any information about a ligand. SGPocket uses the spherical graph convolutional operator working on a spherical relative positioning of amino acids in the protein. Then, a final step of clustering extracts the binding sites. Tested and compared (with well-known binding site prediction methods) on a hand-made dataset, our method performed well and can reduce the docking computing time. Thus, SGPocket allows the reduction of the exploration surface in the molecular docking process by restricting the simulation only to the site(s) predicted to be interesting.

NURECON: A Novel Online System for Determining Nutrition Requirements Based on Microbial Composition.

Dietary habits have been proven to have an impact on the microbial composition and health of the human gut. Over the past decade, researchers have discovered that gut microbiota can use nutrients to produce metabolites that have major implications for human physiology. However, there is no comprehensive system that specifically focuses on identifying nutrient deficiencies based on gut microbiota, making it difficult to interpret and compare gut microbiome data in the literature. This study proposes an analytical platform, NURECON, that can predict nutrient deficiency information in individuals by comparing their metagenomic information to a reference baseline. NURECON integrates a next-generation bacterial 16S rRNA analytical pipeline (QIIME2), metabolic pathway prediction tools (PICRUSt2 and KEGG), and a food compound database (FooDB) to enable the identification of missing nutrients and provide personalized dietary suggestions. Metagenomic information from total number of 287 healthy subjects was used to establish baseline microbial composition and metabolic profiles. The uploaded data is analyzed and compared to the baseline for nutrient deficiency assessment. Visualization results include gut microbial composition, related enzymes, pathways, and nutrient abundance. NURECON is a user-friendly online platform that provides nutritional advice to support dietitians' research or menu design.

In Silico Acute Aquatic Hazard Assessment and Prioritization Using a Grouped Target Site Model: A Case Study of Organic Substances Reported in Permian Basin Hydraulic Fracturing Operations.

Hydraulic fracturing (HF) is commonly used to enhance onshore recovery of oil and gas during production. This process involves the use of a variety of chemicals to support the physical extraction of oil and gas, maintain appropriate conditions downhole (e.g., redox conditions, pH), and limit microbial growth. The diversity of chemicals used in HF presents a significant challenge for risk assessment. The objective of the present study is to establish a transparent, reproducible procedure for estimating 5th percentile acute aquatic hazard concentrations (e.g., acute hazard concentration 5th percentiles [HC5s]) for these substances and validating against existing toxicity data. A simplified, grouped target site model (gTSM) was developed using a database (n = 1696) of diverse compounds with known mode of action (MoA) information. Statistical significance testing was employed to reduce model complexity by combining 11 discrete MoAs into three general hazard groups. The new model was trained and validated using an 80:20 allocation of the experimental database. The gTSM predicts toxicity using a combination of target site water partition coefficients and hazard group-based critical target site concentrations. Model performance was comparable to the original TSM using 40% fewer parameters. Model predictions were judged to be sufficiently reliable and the gTSM was further used to prioritize a subset of reported Permian Basin HF substances for risk evaluation. The gTSM was applied to predict hazard groups, species acute toxicity, and acute HC5s for 186 organic compounds (neutral and ionic). Toxicity predictions and acute HC5 estimates were validated against measured acute toxicity data compiled for HF substances. This case study supports the gTSM as an efficient, cost-effective computational tool for rapid aquatic hazard assessment of diverse organic chemicals. Environ Toxicol Chem 2024;00:1-12. © 2024 ExxonMobil Petroleum and Chemical BV. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Multi-method computational evaluation of the inhibitors against leucine-rich repeat kinase 2 G2019S mutant for Parkinson's disease.

Leucine-rich repeat kinase 2 G2019S mutant (LRRK2 G2019S) is a potential target for Parkinson's disease therapy. In this work, the computational evaluation of the LRRK2 G2019S inhibitors was conducted via a combined approach which contains a preliminary screening of a large database of compounds via similarity and pharmacophore, a secondary selection via structure-based affinity prediction and molecular docking, and a rescoring treatment for the final selection. MD simulations and MM/GBSA calculations were performed to check the agreement between different prediction methods for these inhibitors. 331 experimental ligands were collected, and 170 were used to build the structure-activity relationship. Eight representative ligand structural models were employed in similarity searching and pharmacophore screening over 14 million compounds. The process for selecting proper molecular descriptors provides a successful sample which can be used as a general strategy in QSAR modelling. The rescoring used in this work presents an alternative useful treatment for ranking and selection.

Thiadiazole and Thiazole Derivatives as Potential Antimicrobial Agents.

This review summarizes data on heterocyclic systems with thiadiazole and thiazole fragments in molecules as promising antimicrobial agents. Thiadiazole and thiazole backbones are the most favored and well-known heterocycles, a common and essential feature of various drugs. These scaffolds occupy a central position and are the main structural components of numerous drugs with a wide spectrum of action. These include antimicrobial, antituberculous, anti-inflammatory, analgesic, antiepileptic, antiviral, and anticancer agents. The research is based on bibliosemantic and analytical methods using bibliographic and abstract databases, as well as databases of chemical compounds. This review reports on thiadiazole and thiazole derivatives, which have important pharmacological properties. We are reviewing the structural modifications of various thiadiazole and thiazole derivatives, more specifically, the antimicrobial activity reported over the last years, as we have taken this as our main research area. 80 compounds were illustrated, and various derivatives containing hydrazone bridged thiazole and pyrrole rings, 2-pyridine and 4-pyridine substituted thiazole derivatives, compounds containing di-, tri- and tetrathiazole moieties, Spiro-substituted 4-thiazolidinone-imidazoline-pyridines were analyzed. Derivatives of 5-heteroarylidene-2,4-thiazolidinediones, fluoroquinolone-thiadiazole hybrids, and others. 1,3,4-thiadiazoles and thiazoles are valuable resource for researchers engaged in rational drug design and development in this area.

Predicting the formation of NADES using a transformer-based model

The application of natural deep eutectic solvents (NADES) in the pharmaceutical, agricultural, and food industries represents one of the fastest growing fields of green chemistry, as these mixtures can potentially replace traditional organic solvents. These advances are, however, limited by the development of new NADES which is today, almost exclusively empirically driven and often derivative from known mixtures. To overcome this limitation, we propose the use of a transformer-based machine learning approach. Here, the transformer-based neural network model was first pre-trained to recognize chemical patterns from SMILES representations (unlabeled general chemical data) and then fine-tuned to recognize the patterns in strings that lead to the formation of either stable NADES or simple mixtures of compounds not leading to the formation of stable NADES (binary classification). Because this strategy was adapted from language learning, it allows the use of relatively small datasets and relatively low computational resources. The resulting algorithm is capable of predicting the formation of multiple new stable eutectic mixtures (n = 337) from a general database of natural compounds. More importantly, the system is also able to predict the components and molar ratios needed to render NADES with new molecules (not present in the training database), an aspect that was validated using previously reported NADES as well as by developing multiple novel solvents containing ibuprofen. We believe this strategy has the potential to transform the screening process for NADES as well as the pharmaceutical industry, streamlining the use of bioactive compounds as functional components of liquid formulations, rather than simple solutes.

Discovery of small molecules from natural compound databases as potent retinoid X alpha receptor agonists to treat Alzheimer's disease.

Alzheimer's is characterized as a progressive neurodegenerative disease due to beta-amyloid accumulation in the brain. Some previous studies reported that RXR agonists could be effective in the treatment of Alzheimer's disease. There are currently numerous attempts being made to discover a natural RXR agonist that is more potent than 9-cis-retinoic acid (9CR). One of the most efficient resources for finding high-potential compounds is natural databases. In this study, 81215 compounds from the IB screen library as natural databases were docked against the RXR-alpha binding site. The best compounds discovered interact with the RXR-alpha binding site with a lower binding energy (-11 to -13 kcal/mol) than the binding energy of -10.94 kcal/mol for 9-cis, which means that these compounds could interact stronger with RXR-alpha than 9CR. All selected compounds could pass the blood-brain barrier. Physiochemical properties assessment indicated that all compounds passed Lipinski's rule and had the potential to be oral drug candidates. The stability of protein-ligand complexes during a timescale of 100 ns by Molecular Dynamics simulation demonstrated that all compounds could effectively interact with the RXR binding site. The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) represented that all selected hit compounds had a better binding affinity to the alpha RXR binding site compared to 9CR, which means these hit compounds had potential drug candidates for the treatment of Alzheimer's disease. However, experimental assessment is needed to validate this result.Communicated by Ramaswamy H. Sarma.

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

The fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand-targeting FTO protein is crucial to developing chemotherapeutic medicines to combat obesity and cancer. Scientists worldwide have employed many methodologies to discover a potent inhibitor for the FTO protein. This study uses deep learning-based methods and molecular docking techniques to investigate the FTO protein as a target. Our strategy involves systematically screening a database of small chemical compounds. By utilizing the crystal structures of the FTO complexed with ligands, we successfully identified three small-molecule chemical compounds (ZINC000003643476, ZINC000000517415, and ZINC000001562130) as inhibitors of the FTO protein. The identification process was accomplished by employing a combination of screening techniques, specifically deep learning (DeepBindGCN) and Autodock vina, on the ZINC database. These compounds were subjected to comprehensive analysis using 100 nanoseconds of molecular dynamics and binding free energy calculations. The findings of our study indicate the identification of three candidate inhibitors that might effectively target the human fat mass and obesity protein. The results of this study have the potential to facilitate the exploration of other chemicals that can interact with FTO. Conducting biochemical studies to evaluate these compounds’ effectiveness may contribute to improving fat mass and obesity treatment strategies.

Integrated workflow for the identification of new GABAA R positive allosteric modulators based on the in silico screening with further in vitro validation. Case study using Enamine's stock chemical space.

Numerous studies reported an association between GABAA R subunit genes and epilepsy, eating disorders, autism spectrum disorders, neurodevelopmental disorders, and bipolar disorders. This study was aimed to find some potential positive allosteric modulators and was performed by combining the in silico approach with further in vitro evaluation of its real activity. We started from the GABAA R-diazepam complexes and assembled a lipid embedded protein ensemble to refine it via molecular dynamics (MD) simulation. Then we focused on the interaction of α1β2γ2 with some Z-drugs (non-benzodiazepine compounds) using an Induced Fit Docking (IFD) into the relaxed binding site to generate a pharmacophore model. The pharmacophore model was validated with a reference set and applied to decrease the pre-filtered Enamine database before the main docking procedure. Finally, we succeeded in identifying a set of compounds, which met all features of the docking model. The aqueous solubility and stability of these compounds in mouse plasma were assessed. Then they were tested for the biological activity using the rat Purkinje neurons and CHO cells with heterologously expressed human α1β2γ2 GABAA receptors. Whole-cell patch clamp recordings were used to reveal the GABA induced currents. Our study represents a convenient and tunable model for the discovery of novel positive allosteric modulators of GABAA receptors. A High-throughput virtual screening of the largest available database of chemical compounds resulted in the selection of 23 compounds. Further electrophysiological tests allowed us to determine a set of 3 the most outstanding active compounds. Considering the structural features of leader compounds, the study can develop into the MedChem project soon.

Rational Design of a Potential New Nematicide Targeting Chitin Deacetylase.

In a previously published study, the authors devised a molecular topology QSAR (quantitative structure-activity relationship) approach to detect novel fungicides acting as inhibitors of chitin deacetylase (CDA). Several of the chosen compounds exhibited noteworthy activity. Due to the close relationship between chitin-related proteins present in fungi and other chitin-containing plant-parasitic species, the authors decided to test these molecules against nematodes, based on their negative impact on agriculture. From an overall of 20 fungal CDA inhibitors, six showed to be active against Caenorhabditis elegans. These experimental results made it possible to develop two new molecular topology-based QSAR algorithms for the rational design of potential nematicides with CDA inhibitor activity for crop protection. Linear discriminant analysis was employed to create the two algorithms, one for identifying the chemo-mathematical pattern of commercial nematicides and the other for identifying nematicides with activity on CDA. After creating and validating the QSAR models, the authors screened several natural and synthetic compound databases, searching for alternatives to current nematicides. Finally one compound, the N2-(dimethylsulfamoyl)-N-{2-[(2-methyl-2-propanyl)sulfanyl]ethyl}-N2-phenylglycinamide or nematode chitin deacetylase inhibitor, was selected as the best candidate and was further investigated both in silico, through molecular docking and molecular dynamic simulations, and in vitro, through specific experimental assays. The molecule shows favorable binding behavior on the catalytic pocket of C. elegans CDA and the experimental assays confirm potential nematicide activity.