Ligand-based Virtual Screening Research Articles

Improving drug discovery through parallelism.

Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.

Integrative Ligand-Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Simulation Approaches Identified Potential Lead Compounds against Pancreatic Cancer by Targeting FAK1

Pancreatic cancer is a very deadly disease with a 5-year survival rate, making it one of the leading causes of cancer-related deaths globally. Focal adhesion kinase 1 (FAK1) is a ubiquitously expressed protein in pancreatic cancer. FAK, a tyrosine kinase that is overexpressed in cancer cells, is crucial for the development of tumors into malignant phenotypes. FAK functions in response to extracellular signals by triggering transmembrane receptor signaling, which enhances focal adhesion turnover, cell adhesion, cell migration, and gene expression. The ligand-based drug design approach was used to identify potential compounds against the target protein, which included molecular docking: ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized born surface area (MM-GBSA). Following the retrieval of twenty hits, four compounds were selected for further evaluation based on a molecular docking approach. Three newly discovered compounds, including PubChem CID24601203, CID1893370, and CID16355541, with binding scores of -10.4, -10.1, and -9.7 kcal/mol, respectively, may serve as lead compounds for the treatment of pancreatic cancer associated with FAK1. The ADME (absorption, distribution, metabolism, and excretion) and toxicity analyses demonstrated that the compounds were effective and nontoxic. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the cancer.

Marine bioactive compounds as antibiofilm agent: a metabolomic approach.

The ocean is a treasure trove of both living and nonliving creatures, harboring incredibly diverse group of organisms. A plethora of marine sourced bioactive compounds are discovered over the past few decades, many of which are found to show antibiofilm activity. These are of immense clinical significance since the formation of microbial biofilm is associated with the development of high antibiotic resistance. Biofilms are also responsible to bring about problems associated with industries. In fact, the toilets and wash-basins also show degradation due to development of biofilm on their surfaces. Antimicrobial resistance exhibited by the biofilm can be a potent threat not only for the health care unit along with industries and daily utilities. Various recent studies have shown that the marine members of various kingdom are capable of producing antibiofilm compounds. Many such compounds are with unique structural features and metabolomics approaches are essential to study such large sets of metabolites. Associating holobiome metabolomics with analysis of their chemical attribute may bring new insights on their antibiofilm effect and their applicability as a substitute for conventional antibiotics. The application of computer-aided drug design/discovery (CADD) techniques including neural network approaches and structured-based virtual screening, ligand-based virtual screening in combination with experimental validation techniques may help in the identification of these molecules and evaluation of their drug like properties.

Virtual Screening Based on Ligand and Structure with in vitro Assessment of Neolignans against Trypanosoma cruzi

Chagas disease, caused by the parasite Trypanosoma cruzi, occurs most commonly in Latin America. As the treatment is highly toxic and ineffective in the chronic phase of the disease, alternative treatments are needed. Through quantitative structure-activity relationship modeling (QSAR) analysis using ligand-based and structure-based virtual screening methods, we predicted the trypanocidal potential of 47 neolignans against three targets, the enzymes cruzain, trypanothione reductase, and sterol 14-alpha demethylase. A combined analysis allowed for the selection of potent inhibitors against Trypanosoma cruzi. Of these compounds, two were isolated and shown to inhibit the growth of epimastigotes at concentrations of 9.64 and 8.72 μM, and trypomastigote forms at 4.88 and 2.73 μM. Therefore, the compounds (2R, 3R)-2,3-dihydro-2‑(4‑methoxyphenyl)- 3-methyl-5-(E)-propenylbenzofuran (46) and ottomentosa (47) may be a good option of growth inhibitors for the parasite stages and warrant additional study.

An Effective Solution for Drug Discovery Based on the Tangram Meta-Heuristic and Compound Filtering

Ligand-Based Virtual Screening accelerates and cheapens the design of new drugs. However, it needs efficient optimizers because of the size of compound databases. This work proposes a new method called Tangram CW. The proposal also encloses a knowledge-based filter of compounds. Tangram CW achieves comparable results to the state-of-the-art tools OptiPharm and 2L-GO-Pharm using about a tenth of their computational budget without filtering. Activating it discards more than two thirds of the database while keeping the desired compounds. Thus, it is possible to consider molecular flexibility despite increasing the options. The implemented software package is public.

Ligand-Based Discovery of a Small Molecule as Inhibitor of α-Synuclein Amyloid Formation

α-Synuclein (α-Syn) aggregates are implicated in Parkinson's disease (PD), so inhibitors of α-Syn aggregation have been intensively explored. It has been demonstrated that small molecules might be able to reduce α-Syn aggregation in fibrils, thus exerting neuroprotective effects in models of PD. To expand our knowledge about the structural requirements for blocking the recognition process into the oligomeric assembly of α-Syn aggregates, we performed a ligand-based virtual screening procedure using two well-known α-Syn aggregation inhibitors, SynuClean-D and ZPD-2, as query compounds. A collection of thirty-four compounds bearing distinct chemical functionalities and mutual chemical features were studied in a Th-T fluorescence test, thus identifying 5-(2,6-dinitro-4-(trifluoromethyl)benzyl)-1-methyl-1H-tetrazole (named MeSC-04) as a potent α-Syn amyloid formation inhibitor that demonstrated similar behavior when compared to SynuClean-D in the thioflavin-T-monitored kinetic assays, with both molecules reducing the number and size of amyloid fibrils, as evidenced by electron microscopy. Molecular modeling studies suggested the binding mode of MeSC-04 through the identification of putative druggable pockets on α-syn fibrils and a subsequent consensus docking methodology. Overall, this work could furnish new insights in the development of α-Syn amyloid inhibitors from synthetic sources.

Virtual screening and in vitro experiments highlight cannabidiol as a drug-like phosphodiesterase 9 inhibitor.

The growing interest on the therapeutic potential against neurodegeneration of Cannabis sativa extracts, and of phytocannabinoids in particular, is paralleled by a limited understanding of the undergoing biochemical pathways in which these natural compounds may be involved. Computational tools are nowadays commonly enrolled in the drug discovery workflow and can guide the investigation of macromolecular targets for such molecules. In this contribution, in silico techniques have been applied to the study of C. sativa constituents at various extents, and a total of seven phytocannabinoids and four terpenes were considered. On the side of ligand-based virtual screening, physico-chemical descriptors were computed and evaluated, highlighting the phytocannabinoids possessing suitable drug-like properties to potentially target the central nervous system. Our previous findings and literature data prompted us to investigate the interaction of these molecules with phosphodiesterases (PDEs), a family of enzymes being studied for the development of therapeutic agents against neurodegeneration. Among the compounds, structure-based techniques such as docking and molecular dynamics (MD), highlighted cannabidiol (CBD) as a potential and selective PDE9 ligand, since a promising calculated binding energy value (-9.1 kcal/mol) and a stable interaction in the MD simulation timeframe were predicted. Additionally, PDE9 inhibition assay confirmed the computational results, and showed that CBD inhibits the enzyme in the nanomolar range in vitro, paving the way for further development of this phytocannabinoid as a therapeutic option against neurodegeneration.

Few-Shot Learning for Low-Data Drug Discovery.

The discovery of new hits through ligand-based virtual screening in drug discovery is essentially a low-data problem, as data acquisition is both difficult and expensive. The requirement for large amounts of training data hinders the application of conventional machine learning techniques to this problem domain. This work explores few-shot machine learning for hit discovery and lead optimization. We build on the state-of-the-art and introduce two new metric-based meta-learning techniques, Prototypical and Relation Networks, to this problem domain. We also explore using different embeddings, namely, extended-connectivity fingerprints (ECFP) and embeddings generated through graph convolutional networks (GCN), as inputs to neural networks for classification. This study shows that learned embeddings through GCNs consistently perform better than extended-connectivity fingerprints for toxicity and LBVS experiments. We conclude that the effectiveness of few-shot learning is highly dependent on the nature of the data. Few-shot learning models struggle to perform consistently on MUV and DUD-E data, in which the active compounds are structurally distinct. However, on Tox21 data, the few-shot models perform well, and we find that Prototypical Networks outperform the state-of-the-art, which is based on the Matching Networks architecture. Additionally, training these networks is substantially faster (up to 190%) and therefore takes a fraction of the time to train for comparable, or better, results.

In silico design, synthesis and evaluation of a less toxic octinoxate alternative with suitable photoprotection properties

Excessive UV exposure leads to several skin pathologies such as sunburns, photoaging and carcinogenesis. Currently, sunscreen use is the most important factor in protecting skin from photoinduced damage. Octinoxate is a commonly used UV filter, but its use has become controversial because it acts as an endocrine disruptor in both humans, and marine animals. Research has relied on biotechnology, structure activity relationship (SAR) studies and combinatorial chemistry to find new and less toxic UV filters. However, there are no current examples that describe the possible applications of in silico techniques for obtaining these compounds. Thus, this project sought to design an octinoxate analog that could be used as a less toxic, but equally effective, photoprotective alternative through ligand based virtual screening (LBVS). We designed 213 novel molecules based on the (E)-cinnamoyl moiety of octinoxate, but only 23 were found to be less toxic than the parent compound. Then, an artificial neural network (ANN) based model was built to predict the molar absorptivity of those 23 molecules, and the molecule that presented a similar molar absorptivity to that of octinoxate was chosen for synthesis (analog 4, 3-phenylpropyl (E)-3-(4-methoxyphenyl)acrylate).Synthesis for analog 4 resulted in a 90% yield, and its photoprotective properties, lipophilicity and cytotoxicity were then evaluated. Analog 4 absorbed UV radiation in the range of 250–340 nm, and it presented a molar absorptivity of 36,155 M − 1cm−1. Its lipophilicity was evaluated with RP-HPLC resulting in a logkw of 2.49 and its LC50 was greater than octinoxate's (67.41 nM vs. 45.67 nM). Therefore, results showed that ligand based virtual screening is an effective strategy for the development of new organic UV filters, because it guided the design of less toxic analogs and pinpointed the most likely analog to exhibit UV properties similar to those of octinoxate. In this case, analog 4 is a promising alternative to its parent compound since it proved to be more effective and less toxic.

Review on Compounds Isolated from Eriocaulaceae Family and Evaluation of Biological Activities by Machine Learning.

Eriocaulaceae is a pantropical family whose main center of biodiversity is in Brazil. In general, the family has about 1200 species, in which phytochemical and biological studies have shown a variety of structures and activities. The aim of this research is to compile the compounds isolated in the Eriocaulaceae family and carry out a computational study on their biological targets. The bibliographic research was carried out on six databases. Tables were built and organized according to the chemical class. In addition, a summary of the methods of isolating the compounds was also made. In the computational study were used ChEMBL platform, DRAGON 7.0, and the KNIME 4.4.0 software. Two hundred and twenty-two different compounds have been isolated in sixty-eight species, divided mainly into flavonoids and naphthopyranones, and minor compounds. The ligand-based virtual screening found promising molecules and molecules with multitarget potential, such as xanthones 194, 196, 200 and saponin 202, with xanthone 194 as the most promising. Several compounds with biological activities were isolated in the family, but the chemical profiles of many species are still unknown. The selected structures are a starting point for further studies to develop new antiparasitic and antiviral compounds based on natural products.

Systematic identification of chemical components in Fufang Shuanghua oral liquid and screening of potential active components against SARS-CoV-2 protease

Coronavirus disease (COVID-19) caused by SARS-COV-2 infection has been widely prevalent in many countries and has become a common challenge facing mankind. Traditional Chinese medicine (TCM) has played a prominent role in this pandemic, and especially TCM with the function of “heat-clearing and detoxifying” has shown an excellent role in anti-virus. Fufang Shuanghua oral liquid (FFSH) has been used to treat the corresponding symptoms of influenza such as fever, nasal congestion, runny nose, sore throat, and upper respiratory tract infections in clinic, which are typical symptoms of COVID-19. The content of chlorogenic acid, andrographolide and dehydrated andrographolide as the quality control components of FFSH is not less than 1.0 mg/mL, 60 μg/mL and 60 μg/mL respectively. In this study, UPLC-Q-TOF-MS/MS was employed to describe the chemical profile of FFSH. Virtual screening and fluorescence resonance energy transfer (FRET) were used to screen the effective components of FFSH acting on SARS-CoV-2 main protease (Mpro). As a result, 214 compounds in FFSH were identified or preliminarily characterized by UPLC-Q-TOF-MS/MS, and 61 active ingredients with potential inhibitory effects on Mpro were selected through receptor-based and ligand-based virtual screening. In particular, quercetin, forsythoside A, and linoleic acid showed a good inhibitory effect on Mpro in FRET evaluation with IC50 values of 26.15 μM, 22.26 μM and 47.09 μM respectively, and had a strong binding affinity with the receptor Mpro (6LU7) in molecular docking. CYS145 and HIS41 were the main amino acid residues affected by small molecules in the protein binding domain. In brief, we characterized, for the first time, 214 chemical components in FFSH, and three of them, including quercetin, forsythoside A and linoleic acid, were screened out to exert beneficial anti-COVID-19 effects through CYS145 and HIS41 sites, which may provide a new research strategy for TCM to develop new therapeutic drugs against COVID-19.

Nebivolol as a Potent TRPM8 Channel Blocker: A Drug-Screening Approach through Automated Patch Clamping and Ligand-Based Virtual Screening.

Transient Receptor Potential Melastatin 8 (TRPM8) from the melastatin TRP channel subfamily is a non-selective Ca2+-permeable ion channel with multimodal gating which can be activated by low temperatures and cooling compounds, such as menthol and icilin. Different conditions such as neuropathic pain, cancer, overactive bladder syndrome, migraine, and chronic cough have been linked to the TRPM8 mode of action. Despite the several potent natural and synthetic inhibitors of TRPM8 that have been identified, none of them have been approved for clinical use. The aim of this study was to discover novel blocking TRPM8 agents using automated patch clamp electrophysiology combined with a ligand-based virtual screening based on the SwissSimilarity platform. Among the compounds we have tested, nebivolol and carvedilol exhibited the greatest inhibitory effect, with an IC50 of 0.97 ± 0.15 µM and 9.1 ± 0.6 µM, respectively. This study therefore provides possible candidates for future drug repurposing and suggests promising lead compounds for further optimization as inhibitors of the TRPM8 ion channel.

Ligand-Based Virtual Screening and Molecular Docking of Benzimidazoles as Potential Inhibitors of Triosephosphate Isomerase Identified New Trypanocidal Agents.

Trypanosoma cruzi (T. cruzi) is a parasite that affects humans and other mammals. T. cruzi depends on glycolysis as a source of adenosine triphosphate (ATP) supply, and triosephosphate isomerase (TIM) plays a key role in this metabolic pathway. This enzyme is an attractive target for the design of new trypanocidal drugs. In this study, a ligand-based virtual screening (LBVS) from the ZINC15 database using benzimidazole as a scaffold was accomplished. Later, a molecular docking on the interface of T. cruzi TIM (TcTIM) was performed and the compounds were grouped by interaction profiles. Subsequently, a selection of compounds was made based on cost and availability for in vitro evaluation against blood trypomastigotes. Finally, the compounds were analyzed by molecular dynamics simulation, and physicochemical and pharmacokinetic properties were determined using SwissADME software. A total of 1604 molecules were obtained as potential TcTIM inhibitors. BP2 and BP5 showed trypanocidal activity with half-maximal lytic concentration (LC50) values of 155.86 and 226.30 µM, respectively. Molecular docking and molecular dynamics simulation analyzes showed a favorable docking score of BP5 compound on TcTIM. Additionally, BP5 showed a low docking score (−5.9 Kcal/mol) on human TIM compared to the control ligand (−7.2 Kcal/mol). Both compounds BP2 and BP5 showed good physicochemical and pharmacokinetic properties as new anti-T. cruzi agents.

Phytochemical drug discovery for COVID-19 using high-resolution computational docking and machine learning assisted binder prediction

The COVID-19 pandemic has resulted in millions of deaths around the world. Multiple vaccines are in use, but there are many underserved locations that do not have adequate access to them. Variants may emerge that are highly resistant to existing vaccines, and therefore cheap and readily obtainable therapeutics are needed. Phytochemicals, or plant chemicals, can possibly be such therapeutics. Phytochemicals can be used in a polypharmacological approach, where multiple viral proteins are inhibited and escape mutations are made less likely. Finding the right phytochemicals for viral protein inhibition is challenging, but in-silico screening methods can make this a more tractable problem. In this study, we screen a wide range of natural drug products against a comprehensive set of SARS-CoV-2 proteins using a high-resolution computational workflow. This workflow consists of a structure-based virtual screening (SBVS), where an initial phytochemical library was docked against all selected protein structures. Subsequently, ligand-based virtual screening (LBVS) was employed, where chemical features of 34 lead compounds obtained from the SBVS were used to predict 53 lead compounds from a larger phytochemical library via supervised learning. A computational docking validation of the 53 predicted leads obtained from LBVS revealed that 28 of them elicit strong binding interactions with SARS-CoV-2 proteins. Thus, the inclusion of LBVS resulted in a 4-fold increase in the lead discovery rate. Of the total 62 leads, 18 showed promising pharmacokinetic properties in a computational ADME screening. Collectively, this study demonstrates the advantage of incorporating machine learning elements into a virtual screening workflow. Communicated by Ramaswamy H. Sarma

Critical Assessment of State-of-the-Art Ligand-Based Virtual Screening Methods.

The availability of large chemical libraries containing hundreds of millions to billions of diverse drug-like molecules combined with an almost unlimited amount of compute power to achieve scientific calculations has led investors and researchers to have a renewed interest in virtual screening (VS) methods to identify biologically active compounds. The number of in silico screening tools and software which employ the knowledge of the protein target or known bioactive ligands is increasing at a rapid pace, creating a crowded computational landscape where it has become difficult to assess the real advantages and disadvantages in terms of accuracy and efficiency of each individual VS technology. In the current work, we evaluate the performance of several state-of-the-art commercial software for 3D ligand-based VS against well-known 2D methods using an internally curated benchmarking data set. Our results show that the best individual methods can differ significantly based on the data set, and that combining them using data fusion techniques results in improved enrichment in the top 1 % of retrieved hits. Although 2D methods alone can already provide a significant enrichment in the number of predicted active compounds, the combination of data-fused 2D results with just one out of the best 3D methods (ROCS, FLAP or Blaze) further improves early enrichment and the likelihood of identifying additional chemotypes.

Virtual Screening in the Cloud Identifies Potent and Selective ROS1 Kinase Inhibitors.

ROS1 rearrangements account for 1-2% of non-small cell lung cancer patients, yet there are no specifically designed, selective ROS1 therapies in the clinic. Previous knowledge of potent ROS1 inhibitors with selectivity over TrkA, a selected antitarget, enabled virtual screening as a hit finding approach in this project. The ligand-based virtual screening was focused on identifying molecules with a similar 3D shape and pharmacophore to the known actives. To that end, we turned to the AstraZeneca virtual library, estimated to cover 1015 synthesizable make-on-demand molecules. We used cloud computing-enabled FastROCS technology to search the enumerated 1010 subset of the full virtual space. A small number of specific libraries were prioritized based on the compound properties and a medicinal chemistry assessment and further enumerated with available building blocks. Following the docking evaluation to the ROS1 structure, the most promising hits were synthesized and tested, resulting in the identification of several potent and selective series. The best among them gave a nanomolar ROS1 inhibitor with over 1000-fold selectivity over TrkA and, from the preliminary established SAR, these have the potential to be further optimized. Our prospective study describes how conceptually simple shape-matching approaches can identify potent and selective compounds by searching ultralarge virtual libraries, demonstrating the applicability of such workflows and their importance in early drug discovery.

Virtual Screening of PotentialIn SilicoHits for the Prevention of Neuroinflammation: Arylalkanoic Acid Derivatives of NSAIDs as Selective Dual Inhibitors of Microsomal Prostaglandin E Synthase-2 (mPGES-2) and 5-Lipoxygenase-Activating Protein (FLAP)

The aim of this in silico study was the identification of NSAID’s amides with ability to bypass the COX inhibition and selectively target mPGES-2 and FLAP. Molecular binding analysis was conducted on selected compounds using molecular docking, MD simulations and MM/GBSA calculations to assess their potential in the prevention of neuroinflammation. Ligand-based virtual screening was performed in the ChEMBL database, thereby 196 compounds were obtained. Resulting compounds were passed through the BBB permeability and druglikeness filters, and six compounds were selected for further analysis. Molecular docking analysis showed that compounds 4 and 6 can avoid the COX inhibition and simultaneously target mPGES-2 and FLAP. These two top docked compounds were subjected to MD simulations, which indicated that complexes of compound 4 with mPGES-2 and FLAP have shown conformational stability throughout the simulation. Calculated MM/GBSA binding energies confirmed that compound 4 demonstrated dual binding affinity against mPGES-2 and FLAP. Based on the docking score, MM/GBSA average binding energy and MD results, compound 4 emerged as the best in silico hit for the prevention of neuroinflammation.

Ligand and Structure-Based Virtual Screening in Combination, to Evaluate Small Organic Molecules as Inhibitors for the XIAP Anti-Apoptotic Protein: The Xanthohumol Hypothesis.

Herein, we propose two chalcone molecules, (E)-1-(4-methoxyphenyl)-3-(p-tolyl) prop-2-en-1-one and (E)-3-(4-hydroxyphenyl)-1-(2,4,6-trihydroxyphenyl) prop-2-en-1-one, based on the anticancer bioactive molecule Xanthohumol, which are suitable for further in vitro and in vivo studies. Their ability to create stable complexes with the antiapoptotic X-linked IAP (XIAP) protein makes them promising anticancer agents. The calculations were based on ligand-based and structure-based virtual screening combined with the pharmacophore build. Additionally, the structures passed Lipinski’s rule for drug use, and their reactivity was confirmed using density functional theory studies. ADMET studies were also performed to reveal the pharmacokinetic potential of the compounds. The candidates were chosen from 10,639,400 compounds, and the docking protocols were evaluated using molecular dynamics simulations.

A two-layer mono-objective algorithm based on guided optimization to reduce the computational cost in virtual screening

Virtual screening methods focus on searching molecules with similar properties to a given compound. Molecule databases are made up of large numbers of compounds and are constantly increasing. Therefore, fast and efficient methodologies and tools have to be designed to explore them quickly. In this context, ligand-based virtual screening methods are a well-known and helpful tool. These methods focus on searching for the most similar molecules in a database to a reference one. In this work, we propose a new tool called 2L-GO-Pharm, which requires less computational effort than OptiPharm, an efficient and robust piece of software recently proposed in the literature. The new-implemented tool maintains or improves the quality of the solutions found by OptiPharm, and achieves it by considerably reducing the number of evaluations needed. Some of the strengths that help 2L-GO-Pharm enhance searchability are the reduction of the search space dimension and the introduction of some circular limits for the angular variables. Furthermore, to ensure a trade-off between exploration and exploitation of the search space, it implements a two-layer strategy and a guided search procedure combined with a convergence test on the rotation axis. The performance of 2L-GO-Pharm has been tested by considering two different descriptors, i.e. shape similarity and electrostatic potential. The results show that it saves up to 87.5 million evaluations per query molecule.

Discovery of Novel Small Molecule HDAC1, 2, 3 Inhibitors -- Combined Receptor-Based and Ligand-Based Virtual Screening Strategy

Aims:This study aims to investigate and validate the potential drug target to HDAC1.Background:Human histone deacetylase 1 (HDAC1) can catalyze the deacetylation of histones belonging to the family of human histone deacetylases (HDACs). Amide hydrolase HDAC1 plays a key role in the development of many serious cancers such as prostate cancer, gastric cancer, lung cancer, esophageal cancer, colon cancer, and breast cancer. Therefore, HDAC1 inhibitors, promoting the transcription of a series of key genes such as the p53 gene and inhibiting the development of cancer through various downstream mechanisms, have great potential for the treatment of cancer.Objective:The objective of this study is to discover new skeleton HDAC1 inhibitors efficiently and conveniently with therapeutic potential for cancer.Method:Based on the crystal structure of HDAC1, through the combination of receptor-based and ligand- based virtual screening from the commercial compound library, the top-ranked compounds are selected for purchase through binding modes analysis, and their activities were verified through in vitro HDAC1 inhibitory biological experiments.Results:Based on LeDock, 5ICN showed good distinguishing ability and was used as the receptor. According to the results of the LeDock docking scoring from receptor-based virtual screening, 69 compounds with binding energy less than -7.5 kcal/mol were obtained and used for ligand-based virtual screening. A total of 21 novel compounds with high potential HDAC1 inhibitory activity were collected by combining the similarity searching (NN) and the multinomial Naive Bayes machine learning model (NB) methods. Through binding modes analysis, 10 compounds with different structures with potential HDAC1 inhibitory activity were selected and screened HDAC1 inhibitory in vitro. May267 showed moderate HDAC1 inhibitory activity, and the inhibition rate was 48% at a concentration of 20 μM.Conclusion:This study discovers novel small molecule HDAC1 inhibitors by combined receptor-based and ligand-based virtual screening strategy, which provides an efficient method for the discovery of other small molecule drugs. May267 shows moderate HDAC1 inhibitory activity, which can be further optimized as a lead compound. However, it still has the problem of poor kinase selectivity to be solved.