Computer-assisted Drug Design Research Articles

InVADo: Interactive Visual Analysis of Molecular Docking Data.

Molecular docking is a key technique in various fields like structural biology, medicinal chemistry, and biotechnology. It is widely used for virtual screening during drug discovery, computer-assisted drug design, and protein engineering. A general molecular docking process consists of the target and ligand selection, their preparation, and the docking process itself, followed by the evaluation of the results. However, the most commonly used docking software provides no or very basic evaluation possibilities. Scripting and external molecular viewers are often used, which are not designed for an efficient analysis of docking results. Therefore, we developed InVADo, a comprehensive interactive visual analysis tool for large docking data. It consists of multiple linked 2D and 3D views. It filters and spatially clusters the data, and enriches it with post-docking analysis results of protein-ligand interactions and functional groups, to enable well-founded decision-making. In an exemplary case study, domain experts confirmed that InVADo facilitates and accelerates the analysis workflow. They rated it as a convenient, comprehensive, and feature-rich tool, especially useful for virtual screening.

Pairwise Additivity and Three-Body Contributions for Density Functional Theory-Based Protein-Ligand Interaction Energies.

The prediction of protein-ligand binding energies is crucial in computer-assisted drug design. This property can be calculated in a straightforward fashion as the difference in the energies between a binding site-ligand complex and the separated binding site and ligand. Often, though, there is value in knowing how different amino acid residues in the protein binding site interact with the ligand. In this case, the interaction energy can be calculated as the sum of pairwise energies between each amino acid residue in the binding site and the ligand, and the sum of these energies is often equated with the total interaction energy. The validity of this pairwise additivity approximation can be assessed by experimental evidence, such as double-mutant cycles. In this work, we test the pairwise additivity approximation on the sulfotransferase-l-DOPA complex for 16 density functional theory (DFT) methods with varying degrees of exact (Hartree-Fock) exchange. Several "families" of functionals are studied, including BLYP, B3LYP, and CAM-B3LYP, as well as M06L, M06, and M062X. We also calculate the three-body contributions to interaction energy for the same DFT methods and assess when they are significant. We find that the amount of exact exchange or other nonlocal contributions has a direct influence on how closely the sum of pairwise energies approximates the total interaction energy. We also find that three-body interactions can be significant and that their significance can be predicted with good accuracy.

UNREVEALING THE COMPLEX INTERPLAY: MOLECULAR DOCKING: A COMPREHENSIVE REVIEW ON CURRENT SCENARIO, UPCOMING DIFFICULTIES, FORTHCOMING INITIATIVES, AND VIEWPOINTS

The computer modelling of structural complexes generated from two or more interacting molecules are referred to as molecular docking. It is an indispensable tool in computer-aided drug design and structural molecular biology. Using this technology, large libraries of compounds may be digitally screened, and the results can be graded along with structural assumptions about how the ligands impact the target's reduction. Recent advances in the synthesis of anti-infectious medicines prompted by structural insights have enabled the application of computer-assisted drug design in the quest for innovative mechanism-or structure-based drugs. Molecular docking is an important phase in the drug development process because it determines the best positions for molecules to occupy when they are coupled together and predicts how effectively two molecules will bind once they have been docked. The input structure's design is also critical, and the results are assessed using sampling methods and scoring systems. The recently developed docking software Local Move Monte Carlo provides a strong choice for customizable receptor docking strategies. Docking is a technique for determining how ligands and proteins interact. It is structurally sound and compatible with computer-assisted medication design. Successful docking discovers high-dimensional spaces and ranks function utilisation, resulting in a candidate docking rating that is acceptable. It may also be used to screen vast libraries of molecules and offer structural hypotheses for the process.

Rudimentary Review on Molecular Docking: A Beginner’s Guide

The computer modelling of structural complexes generated from two or more interacting molecules are referred to as molecular docking. It is an indispensable tool in computer-aided drug design and structural molecular biology. Using this technology, large libraries of compounds may be digitally screened, and the results can be graded along with structural assumptions about how the ligands impact the target's reduction. Recent advances in the synthesis of anti-infectious medicines prompted by structural insights have enabled the application of computer-assisted drug design in the quest for innovative mechanism- or structure-based drugs. Molecular docking is an important phase in the drug development process because it determines the best positions for molecules to occupy when they are coupled together and predicts how effectively two molecules will bind once they have been docked. The input structure's design is also critical, and the results are assessed using sampling methods and scoring systems. The recently developed docking software Local Move Monte Carlo [LMMC] provides a strong choice for customizable receptor docking strategies. Docking is a technique for determining how ligands and proteins interact. It is structurally sound and compatible with computer-assisted medication design. Successful docking discovers high-dimensional spaces and ranks function utilisation, resulting in a candidate docking rating that is acceptable. It may also be used to screen vast libraries of molecules and offer structural hypotheses for the process.

Prediction of Small-Molecule Developability Using Large-Scale In Silico ADMET Models.

Early in silico assessment of the potential of a series of compounds to deliver a drug is one of the major challenges in computer-assisted drug design. The goal is to identify the right chemical series of compounds out of a large chemical space to then subsequently prioritize the molecules with the highest potential to become a drug. Although multiple approaches to assess compounds have been developed over decades, the quality of these predictors is often not good enough and compounds that agree with the respective estimates are not necessarily druglike. Here, we report a novel deep learning approach that leverages large-scale predictions of ∼100 ADMET assays to assess the potential of a compound to become a relevant drug candidate. The resulting score, which we termed bPK score, substantially outperforms previous approaches and showed strong discriminative performance on data sets where previous approaches did not.

A Theobromine Derivative with Anticancer Properties Targeting VEGFR-2: Semisynthesis, in silico and in vitro Studies.

A computer-assisted drug design (CADD) approach was utilized to design a new acetamido-N-(para-fluorophenyl)benzamide) derivative of the naturally occurring alkaloid, theobromine, (T-1-APFPB), following the pharmacophoric features of VEGFR-2 inhibitors. The stability and reactivity of T-1-AFPB were assessed through density functional theory (DFT) calculations. Molecular docking assessments showed T-1-AFPB's potential to bind with and inhibit VEGFR-2. The precise binding of T-1-AFPB against VEGFR-2 with optimal energy was further confirmed through several molecular dynamics (MD) simulations, PLIP, MM-GBSA, and PCA studies. Then, T-1-AFPB (4-(2-(3,7-Dimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-1-yl)acetamido)-N-(4-fluorophenyl)benzamide) was semi-synthesized and the in vitro assays showed its potential to inhibit VEGFR-2 with an IC50 value of 69 nM (sorafenib's IC50 was 56 nM) and to inhibit the growth of HepG2 and MCF-7 cancer cell lines with IC50 values of 2.24±0.02 and 3.26±0.02 μM, respectively. Moreover, T-1-AFPB displayed very high selectivity indices against normal Vero cell lines. Furthermore, T-1-AFPB induced early (from 0.72 to 19.12) and late (from 0.13 to 6.37) apoptosis in HepG2 cell lines. In conclusion, the combined computational and experimental approaches demonstrated the efficacy and safety of T-1-APFPB providing it as a promising lead VEGFR-2 inhibitor for further development aiming at cancer therapy.

Docking Studies on Biomolecules from Marine Microalga Skeletonema costatum Against Hemolysin Protein of Bioluminescence Disease-Causing Vibrio harveyi.

Grow-out and hatchery units of shrimps are being impacted by disease-causing bacterial pathogens and predominantly marine Vibrios. The use of chemicals for governing bacterial pathogens in the aquaculture practices is developing resistance to bacteria. Henceforth, the application of bio-therapeutic agents from marine resources for controlling pathogens is most vital to be considered. Molecular docking is computer-assisted drug design tool to detect and counteract for drug-receptor interaction for known target protein of diseases. Therefore, an effort was made with the extract of the marine micro alga Skeletonema costatum against hemolysin protein of pathogenic bacteria Vibrio harveyi. The extract of S. costatum was tested against growth and virulence produced by V. harveyi during larviculture of Penaeus monodon. The extract was analyzed for phyto-constituents through GC-MS and used them as ligand molecule in docking. S. costatum extract at 200µgmL-1 was found to decrease 35.20% of cumulative percentage mortality (CPM) in postlarvae of P. monodon against V. harveyi infections. The biomolecule Docasane, an alkane from the extract of S. costatum, exposed highest binding interaction than other compounds during docking analysis. The level of significance (P < 0.05) was found in CPM, growth, and virulence factors of V. harveyi studies. Thus, the present finding predicts that extract of S. costatum containing biomolecules can be recommended for use in the shrimp culture-based grow-out and hatchery units for eliminating bioluminescent V. harveyi.

Computer-Aided Identification of Kinase-Targeted Small Molecules for Cancer: A Review on AKT Protein.

AKT (also known as PKB) is a serine/threonine kinase that plays a pivotal regulatory role in the PI3K/AKT/mTOR signaling pathway. Dysregulation of AKT activity, especially its hyperactivation, is closely associated with the development of various human cancers and resistance to chemotherapy. Over the years, a wide array of AKT inhibitors has been discovered through experimental and computational approaches. In this regard, herein we present a comprehensive overview of AKT inhibitors identified using computer-assisted drug design methodologies (including docking-based and pharmacophore-based virtual screening, machine learning, and quantitative structure-activity relationships) and successfully validated small molecules endowed with anticancer activity. Thus, this review provides valuable insights to support scientists focused on AKT inhibition for cancer treatment and suggests untapped directions for future computer-aided drug discovery efforts.

Molecular docking approaches of biomolecules extracted from red seaweed Kappaphycus alvarezii against hemolysin protein of bioluminescence disease-causing bacteria Vibrio harveyi

Shrimp culture systems are being affected by bacterial diseases particularly Vibrios. Use of therapeutic agents in shrimp culture has lead to the resistance among aquatic bacteria. Accordingly, health management becomes of major importance in aquaculture. Under this situation, developing bio-inhibitors from natural marine resources which are most appropriate, which is considered as an unmatched reservoir of several biological products. Molecular docking is suited to a major approach in structural biology and computer-assisted drug design against the proteins of disease-causative agents. In the present study, marine macroalga Kappaphycus alvarezii was used for developing inhibitors against bioluminescence disease-causing pathogenic bacteria Vibrio harveyi. K. alvarezii was collected from the Mandapam, Tamil Nadu, India. Ethyl acetate based extract of K. alvarezii was tested against the growth and virulence factors of V. harveyi during Penaeus monodon larviculture. Further K. alvarezii extract was subjected to GC–MS analysis to identify the biomolecules. The homology modelling of the virulent protein, hemolysin of V. harveyi was designed during this study. Hence, it was aimed at molecular docking against the biomolecules identified from K. alvarezii extract. During shrimp larviculture, the extract of K. alvarezii (200 μg mL−1) exhibited a reduction on Cumulative Percentage of Mortality (29.70 %) in postlarvae against the challenge of V. harveyi infection. Among the compounds docked, an inhibitory effect was observed based on docking scores and were found the highest binding affinity/inhibiting activity in Cyclotetracosane with the least energy required with a docking score of binding energy −7.66 kcal/mol, inhibition constant 0.002 mM and intermolecular efficiency −0.32 kcal/mol. Using statistical analysis, significant differences (p < 0.05) were observed between the growth and virulence factors of V. harveyi during shrimp larviculture trials. While considering these findings, it was determined that K. alvarezii extract can be replaced as an alternative bio-agents protecting against infections caused by V. harveyi and possibly other aquatic pathogenic bacteria in shrimp farming systems.

An in-silico investigation based on molecular simulations of novel and potential brain-penetrant GluN2B NMDA receptor antagonists as anti-stroke therapeutic agents

GluN2B-induced activation of NMDA receptors plays a key function in central nervous system (CNS) disorders, including Parkinson, Alzheimer, and stroke, as it is strongly involved in excitotoxicity, which makes selective NMDA receptor antagonists one of the potential therapeutic agents for the treatment of neurodegenerative diseases, especially stroke. The present study aims to examine a structural family of thirty brain-penetrating GluN2B N-methyl-D-aspartate (NMDA) receptor antagonists, using virtual computer-assisted drug design (CADD) to discover highly candidate drugs for ischemic strokes. Initially, the physicochemical and ADMET pharmacokinetic properties confirmed that C13 and C22 compounds were predicted as non-toxic inhibitors of CYP2D6 and CYP3A4 cytochromes, with human intestinal absorption (HIA) exceeding 90%, and designed to be as efficient central nervous system (CNS) agents due to the highest probability to cross the blood-brain barrier (BBB). Compared to ifenprodil, a co-crystallized ligand complexed with the transport protein encoded as 3QEL.pdb, we have noticed that C13 and C22 chemical compounds were defined by good ADME-Toxicity profiles, meeting Lipinski, Veber, Egan, Ghose, and Muegge rules. The molecular docking results indicated that C22 and C13 ligands react specifically with the amino acid residues of the NMDA receptor subunit GluN1 and GluN2B. These intermolecular interactions produced between the candidate drugs and the targeted protein in the B chain remain stable over 200 nanoseconds of molecular dynamics simulation time. In conclusion, C22 and C13 ligands are highly recommended as anti-stroke therapeutic drugs due to their safety and molecular stability towards NMDA receptors. Communicated by Ramaswamy H. Sarma

Panoramic Review on Progress and Development of Molecular Docking

In structural molecular biology and computer-assisted drug creation, molecular docking is a crucial tool. Predicting the prevailing binding mode (s) of a ligand with a protein having a known three-dimensional structure is the aim of ligand-protein docking. Effective docking methods use a scoring system that correctly ranks candidate dockings and efficiently explore high-dimensional spaces. Lead optimization benefits greatly from the use of docking to do virtual screening on huge libraries of compounds, rate the outcomes, and offer structural ideas for how the ligands inhibit the target. It can be difficult to interpret the findings of stochastic search methods, and setting up the input structures for docking is just as crucial as docking itself. In recent years, computer-assisted drug design has relied heavily on the molecular docking technique to estimate the binding affinity and assess the interactive mode since it can significantly increase efficiency and lower research costs. The main concepts, techniques, and frequently utilized molecular docking applications are introduced in this work. Additionally, it contrasts the most popular docking applications and suggests relevant study fields. Finally, a brief summary of recent developments in molecular docking, including the integrated technique and deep learning, is provided. Current docking applications are not precise enough to forecast the binding affinity due to the insufficient molecular structure and the inadequacies of the scoring mechanism.

Computer-Aided drug design of new 2-amino-thiophene derivatives as anti-leishmanial agents

The leishmaniasis is a neglected disease caused by a group of protozoan parasites from the genus Leishmania whose treatment is limited, obsolete, toxic, and ineffective in certain cases. These characteristics motivate researchers worldwide to plan new therapeutic alternatives for the treatment of leishmaniasis, where the use of cheminformatics tools applied to computer-assisted drug design has allowed research to make great advances in the search for new drugs candidates. In this study, a series of 2-amino-thiophene (2-AT) derivatives was screened virtually using QSAR tools, ADMET filters and prediction models, allowing direct the synthesis of compounds, which were evaluated in vitro against promastigotes and axenic amastigotes of Leishmania amazonensis. The combination of different descriptors and machine learning methods led to obtaining robust and predictive QSAR models, which was obtained from a dataset composed of 1862 compounds extracted from the ChEMBL database, with correct classification rates ranging from 0.53 (for amastigotes) to 0.91 (for promastigotes), allowing to select eleven 2-AT derivatives, which do not violate Lipinski's rules, exhibit good druglikeness, and with probability ≤70% of potential activity against the two evolutionary forms of the parasite. All compounds were properly synthesized and 8 of them were shown to be active at least against one of the evolutionary forms of the parasite with IC50 values lower than 10 μM, being more active than the reference drug meglumine antimoniate, and showing low or no citotoxicity against macrophage J774.A1 for the most part. Compounds 8CN and DCN-83, respectively, are the most active against promastigote and amastigote forms, with IC50 values of 1.20 and 0.71 μM, and selectivity indexes (SI) of 36.58 and 119.33. Structure Activity Relationship (SAR) study was carried out and allowed to identify some favorable and/or essential substitution patterns for the leishmanial activity of 2-AT derivatives. Taken together, these findings demonstrate that the use of ligand-based virtual screening proved to be quite effective and saved time, effort, and money in the selection of potential anti-leishmanial agents, and confirm, once again that 2-AT derivatives are promising hit compounds for the development of new anti-leishmanial agents.

Computer Assisted Drug Design of AITC for Treatment of Cancer: A Combined Molecular Target Identification Study and Drug-Target Prediction

Drugs can influence the whole biological system by targeting interaction reactions. However, current databases have minimal drug target information and only a few are validated. This emerges a need to develop new drugs that predict true interaction pairs with high accuracy. Currently, network pharmacology has been used in identifying potential drug targets for allylisothiocyanate (AITC) and in predicting the spread of its activity toward bladder cancer whereas the mechanisms of isothiocyanates remain largely undefined. In this study, we present a computational method to predict targets for AITC. Results showed 13 differentially expressed hubs confirmed by Cytoscape (3.7.2). We further observed that the obtained targets were found to involve a type of Ontology of classification- molecular function, cellular component, biological process and protein class. Pathway enrichment was done by DAVID. This study identifies key genes and pathways involved in bladder cancer which helps us to understand the progression and molecular mechanism underlying bladder cancer. The obtained key genes may serve as active therapeutic targets for bladder cancer treatment. AITC may directly regulate several biological processes and their genes in bladder cancer.

Synthesis and molecular docking investigations of some benzofuran derivatives containing 1,2,4-triazole moiety as potent antibacterial and antifungal agents

Inspired by the pharmacological activities of 1, 2, 4-triazoles, a new series of 1, 2, 4-triazoles derivatives was prepared by using the key intermediate 3-amino-5-nitro-1-benzofuran-2-yl) (phenyl) methanone (2). Compound-2 on acetylation and benzoylation yielded corresponding derivatives 3a-b, which were then treated with hydrazine hydrate to produce compound (4a-b). Compounds (4a-b) when refluxed with glacial acetic acid, produce targeted molecules of 1, 2, 4-triazoles compounds (5a-b). All the newly synthesized compounds were characterized by FT IR, 1H NMR and mass spectral analysis. All the compounds were screened for their in vitro antibacterial activity and antifungal activity. Structural molecular biology and computer-assisted drug design of molecular docking studies were made on the pore-forming domain of Escherichia coli. The immunity protein of coli E7 (ImmE7) protein inhibiters of bactericidal activity (Protein Data Bank: 1COL) binding the compounds well in the active site of protein has remarkable hydrogen bonding interactions.

CAT-Site: Predicting Protein Binding Sites Using a Convolutional Neural Network.

Identifying binding sites on the protein surface is an important part of computer-assisted drug design processes. Reliable prediction of binding sites not only assists with docking algorithms, but it can also explain the possible side-effects of a potential drug as well as its efficiency. In this work, we propose a novel workflow for predicting possible binding sites of a ligand on a protein surface. We use proteins from the PDBbind and sc-PDB databases, from which we combine available ligand information for similar proteins using all the possible ligands rather than only a special sub-selection to generalize the work of existing research. After performing protein clustering and merging of ligands of similar proteins, we use a three-dimensional convolutional neural network that takes into account the spatial structure of a protein. Lastly, we combine ligandability predictions for points on protein surfaces into joint binding sites. Analysis of our model's performance shows that its achieved sensitivity is 0.829, specificity is 0.98, and F1 score is 0.517, and that for 54% of larger and pharmacologically relevant binding sites, the distance between their real and predicted centers amounts to less than 4 Å.

SubGE: Enhancing the subgraph representation of molecular compounds structure–activity relationship discovery

Prediction of the molecular compound structure–activity relationship is one of the most critical tasks in computer-assisted drug design. To accurately predict the properties of molecular compounds and explain their structure–activity relationships, we proposed a subgraph embedding model, subGE, based on reinforcement learning and mutual information mechanisms. First, molecular compounds were abstracted into graphs, and the original graphs were sampled using a breadth-first search. These subgraphs were then encoded using graph neural networks and converted into graph embeddings. Reinforcement learning was introduced to reduce the dimensionality of the subgraph embeddings and filter out significant subgraphs. A mutual information mechanism was introduced to further enhance the ability of the filtered subgraphs to characterize a full graph. SubGE was evaluated based on three open-source datasets, BBBP, Bace, and Clintox from the DeepChem package developed by MoleculeNet. The experimental results showed that subGE achieved accuracies of 86.61%, 80.49%, 95.81%, and 96.34% for four classification tasks with three datasets. These values represent improvements of 16.87%, 19.29%, 3.91%, and 3.73%, respectively, compared to that of existing graph convolutional networks, and of 8.34%, 6.64%, 5.53%, and 5.50%, respectively, compared to that of the direct encoding of subgraphs without introducing reinforcement learning and mutual information mechanisms. The subgraphs extracted by subGE could fully explain the conformational relationships of compounds through visualization.

Molecular Docking Analysis and ADMET Properties of Isoquinoline Derivatives as Potential Acetylcholinesterase Inhibitors

Alzheimer’s disease (AD) are the progressive necrosis of central cholinergic neurons, followed by intra and hippocampal acetylcholine (ACh) levels. Acetylcholinesterase Enzyme (AChE) is an enzyme that is responsible for the metabolic breakdown of acetylcholine (ACh). The hydrolysis of ACh by excess amount of AChE decreases the amount of ACh in the brain, thus interfering with the normal brain functions. Modelling of seven isoquinoline derivative compounds is done by using Computer-Assisted Drug Design (CADD) such as molecular docking and ADMET properties calculation. The results obtained show that compound 13, 14, 16, and 17 have a lower binding energy about -9 kcal/mol. It can be caused due to the more electron donating groups in these compounds. ADMET properties calculation is attempted to evaluate the interaction between the four compounds (13, 14, 16, and 17) when entering the body systems. ADMET calculation result is showing that compounds 13 and 14 fulfill the parameter in adsorption properties while the others are not. Indicating that compounds 13 and 14 are potential to be Alzheimer’s drug. This result is in line with experimental study.

IN SILICO STUDY OF BIOACTIVE COMPOUNDS FROM SUNGKAI (PERONEMA CANESCENS) AS IMMUNOMODULATOR

Objective: This study aims to predict a bioactive compound from Peronema canescens (PC) with mechanisms inhibitor interleukin 6 (IL-6) and tumor necrosis factor-alpha (TNF-α) potential as an immunomodulatory using in silico approach.&#x0D; Methods: Autodock 4 was used to accomplish computer-assisted drug design with molecular docking simulation to discover binding energy, inhibition constant, and interactions with an amino acid in bioactive compounds from PC against IL-6 and TNF-α receptors. Lipinski predicts the drug-likeness of a bioactive compound for the oral route of administration. ADMET profiling of bioactive compounds to predict pharmacokinetic properties with pkCSM ADMET.&#x0D; Results: The results showed that the best binding energy, inhibition constant, and interactions with an amino acid of peronemin C1 against IL-6 and TNF-α receptors were (-7.19 kcal/mol; 5.39 nM; Arg 179, Arg 182, Gln 175), and (-8.86 kcal/mol; 320.42 nM; Tyr 119, Tyr 59, and Gly 121), respectively. All bioactive compounds from PC met Lipinski's rule of five requirements for oral administration. ADMET prediction results all bioactive compounds from PC are non-mutagenic, except peronemin D1 is mutagenic.&#x0D; Conclusion: The peronemin C1 bioactive compounds from PC have good immunomodulatory potential, effectively inhibiting human IL-6 and TNF-α receptors using in silico approach.

Review on Computer-Aided Drug Design (CADD) Technique in Drug Discovery Researches and Solvent-Free Synthesis of 3,4-Dihydropyrimidin-2-(1H)-Ones/Thiones Catalyzed by Camphor Sulfonic Acid

Combined with various computational and theoretical approaches, computer-aided drug design (CADD) is one of the contemporary approaches to drug discovery. Furthermore, this study briefly assesses the literature on computer-assisted drug design (CADD) techniques used in drug discovery research. An environmentally friendly synthetic route was developed for the convenient Biginelli synthesis of 3,4-dihydropyridine-2-(1H)-ones/thiones when no solvents are involved in the reaction using aromatic derivatives, urea/thiourea, and ethyl/methyl acetoacetate as catalysts in combination with camphor sulfonic acid (CSA). All reactions proceeded rapidly, and good product yields were obtained. Green synthesis in this study has many advantages over conventional synthesis, including a clean and environmentally friendly synthesis, easy operation, the elimination of hazardous organic solvents, a purification step free of chromatography, and the use of a non-corrosive and non-volatile catalyst. Furthermore, the method can be applied to single-tank reactions in a solvent-free environment.

Computational Methods in Cooperation with Experimental Approaches to Design Protein Tyrosine Phosphatase 1B Inhibitors in Type 2 Diabetes Drug Design: A Review of the Achievements of This Century.

Protein tyrosine phosphatase 1B (PTP1B) dephosphorylates phosphotyrosine residues and is an important regulator of several signaling pathways, such as insulin, leptin, and the ErbB signaling network, among others. Therefore, this enzyme is considered an attractive target to design new drugs against type 2 diabetes, obesity, and cancer. To date, a wide variety of PTP1B inhibitors that have been developed by experimental and computational approaches. In this review, we summarize the achievements with respect to PTP1B inhibitors discovered by applying computer-assisted drug design methodologies (virtual screening, molecular docking, pharmacophore modeling, and quantitative structure–activity relationships (QSAR)) as the principal strategy, in cooperation with experimental approaches, covering articles published from the beginning of the century until the time this review was submitted, with a focus on studies conducted with the aim of discovering new drugs against type 2 diabetes. This review encourages the use of computational techniques and includes helpful information that increases the knowledge generated to date about PTP1B inhibition, with a positive impact on the route toward obtaining a new drug against type 2 diabetes with PTP1B as a molecular target.