Virtual Screening Of Small Molecules Research Articles

ROSHAMBO: Open-Source Molecular Alignment and 3D Similarity Scoring.

Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and offers an approach complementary to structure-based virtual screening. Our package employs the PAPER software for optimizing molecular alignments based on Gaussian volume overlaps. GPU acceleration is utilized to significantly reduce computational time and resource requirements. After obtaining the optimal alignments between the target and the query molecules, both shape and color (based on pharmacophore features) scores are computed to assess molecular similarity, with aligned molecules optionally being output in sdf format. The package was benchmarked using the DUDE-Z public data sets. Results demonstrated the package's near-state-of-the-art performance and robustness across multiple target classes, with speed that enables many routine ligand-based drug discovery workflows. As an open-source and freely available resource (github.com/molecularinformatics/roshambo) with both a convenient Python API and command line interface, our package also addresses the need for accessible and efficient virtual screening tools in drug discovery.

Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation.

This study aimed to identify potential BCL-2 small molecule inhibitors using deep neural networks (DNN) and random forest (RF), algorithms as well as molecular docking and molecular dynamics (MD) simulations to screen a library of small molecules. The RF model classified 61% (2355/3867) of molecules as 'Active'. Further analysis through molecular docking with Vina identified CHEMBL3940231, CHEMBL3938023, and CHEMBL3947358 as top-scored small molecules with docking scores of -11, -10.9, and 10.8 kcal/mol, respectively. MD simulations validated these compounds' stability and binding affinity to the BCL2 protein.

Structural and functional characterization of 1-deoxy-D-xylulose-5-phosphate synthase (DXS) from Acinetobacter baumannii: identification of promising lead molecules from virtual screening, molecular docking and molecular dynamics simulations

The advent of multi drug resistance and extensive-drug resistance among various pathogens has caused a rise in nosocomial infections, which in turn has led to rising hospital-acquired infection-related mortality rates. Amongst them, carbapenem-resistant Acinetobacter baumannii is one of the most notorious bacterial species, categorized as a Priority 1: Critical pathogen by the WHO. Therefore, the discovery and development of novel antibiotics, as well as the identification of potential inhibitors, have become the need of the hour. In this study, we have employed computational methods to explore and identify molecules capable of inhibiting enzymes essential in the methylerythritol 4-phosphate (MEP) biosynthetic pathway. The high throughput virtual screening of small molecules (Enamine Advanced Collection (AC) library) against the highly conserved substrate-binding site of the DXS target protein provided us with a total of 1000 molecules. The top four potential candidate molecules, namely—Z3353989070, Z3353989049, Z2295848528, and Z1685501455, alongside fluoropyruvate (control), a known inhibitor of DXS, was chosen for a molecular dynamic simulation study. The molecular dynamic simulation trajectories suggested high structural and thermodynamical stability and strong binding affinity of all the DXS-ligand complexes. Moreover, the MM/PBSA-based binding free energy calculations also exhibited strong interactions of the selected ligand molecules with DXS. In conclusion, we have found that all four molecules displayed better results and stronger binding affinity than the control. In the end, based on all the above-mentioned criteria, we have proposed Z3353989049 to be the promising lead candidate against DXS from A. baumannii. Communicated by Ramaswamy H. Sarma

Elucidating novel antibacterial compounds from the NPASS database against the FimH lectin domain for the treatment of urinary tract infections: an in-silico study

The increase in multidrug-resistant pathogens in urinary tract infections (UTIs) among communities and hospitals threatens our ability to treat these common pathogens. Uropathogenic Escherichia coli (UPEC) strains are the most frequent uropathies linked to the development of UTIs. This work aims to introduce bioactive natural products via virtual screening of small molecules from a public database to prevent biofilm formation by inhibiting FimH, a type 1 fimbriae that plays a crucial role in UPEC pathogenicity. A total of 30926 small molecules from the NPASS database were subjected to screening via molecular docking. Followed by performing in silico ADME studies, seven molecules showed promising docking results ranging from −6.8 to −8.7 kcal/mol. As a result of the docking score findings, 100 ns Molecular dynamics (MD) simulations were performed. Based on MM-PBSA analysis, NPC313334 ligand showed high binding affinity −42 and stability with the binding pocket of FimH protein during molecular dynamic simulations. DFT calculations were also performed on the ligands to calculate the HOMO-LUMO energies of the compounds in order to an idea about their structure and reactivity. This research suggests that NPC313334 may be a possible antibacterial drug candidate that targets FimH to reduce the number of UPEC-related urinary tract infections. Communicated by Ramaswamy H. Sarma

The \u03b4 subunit of F1Fo-ATP synthase is required for pathogenicity of Candida albicans

Fungal infections, especially candidiasis and aspergillosis, claim a high fatality rate. Fungal cell growth and function requires ATP, which is synthesized mainly through oxidative phosphorylation, with the key enzyme being F1Fo-ATP synthase. Here, we show that deletion of the Candida albicans gene encoding the δ subunit of the F1Fo-ATP synthase (ATP16) abrogates lethal infection in a mouse model of systemic candidiasis. The deletion does not substantially affect in vitro fungal growth or intracellular ATP concentrations, because the decrease in oxidative phosphorylation-derived ATP synthesis is compensated by enhanced glycolysis. However, the ATP16-deleted mutant displays decreased phosphofructokinase activity, leading to low fructose 1,6-bisphosphate levels, reduced activity of Ras1-dependent and -independent cAMP-PKA pathways, downregulation of virulence factors, and reduced pathogenicity. A structure-based virtual screening of small molecules leads to identification of a compound potentially targeting the δ subunit of fungal F1Fo-ATP synthases. The compound induces in vitro phenotypes similar to those observed in the ATP16-deleted mutant, and protects mice from succumbing to invasive candidiasis. Our findings indicate that F1Fo-ATP synthase δ subunit is required for C. albicans lethal infection and represents a potential therapeutic target.

Theoretical investigation of selective ligand binding mode of galanin receptors

The Galaninergic system consist of Galanin and its receptors, involved in neuromodulation and neurotransmission. Galanin regulate its physiologic and pathologic functions by interacting with three G-protein coupled receptors; GalR1, GalR2 and GalR3. The widespread distribution of Galanin and its receptor subtypes in central and peripheral nervous system makes them an attractive drug target for the treatment of neurological diseases. However, subtypes selective ligands paucity and little structural information related to either Galanin receptors and Galanin receptor-ligand complexes hampered the structure-based drug design. Thus computational modeling characterization strategy was utilized for Galanin receptor 3D structure prediction and subtypes ligands binding selectivity. Reported ligands with experimental activity were docked against the homology model of Galanin receptors. Further, the MD simulation and binding free energy calculation were carried out to determine the binding interactions pattern consistency and selectivity towards receptor subtype. Results of binding free energy of per residue indicate key contribution of GalR1 Phe115 and His267 in the selective binding of ligands while Tyr103, Tyr270 and His277 play major role in the selective binding of GalR3 ligands. Our study provide rationale for further in silico virtual screening of small molecules for the development of selective ligands against Galanin receptor subtypes. Communicated by Ramaswamy H. Sarma

DockECR: Open consensus docking and ranking protocol for virtual screening of small molecules

The development of open computational pipelines to accelerate the discovery of treatments for emerging diseases allows finding novel solutions in shorter periods of time. Consensus molecular docking is one of these approaches, and its main purpose is to increase the detection of real actives within virtual screening campaigns. Here we present dockECR, an open consensus docking and ranking protocol that implements the exponential consensus ranking method to prioritize molecular candidates. The protocol uses four open source molecular docking programs: AutoDock Vina, Smina, LeDock and rDock, to rank the molecules. In addition, we introduce a scoring strategy based on the average RMSD obtained from comparing the best poses from each single program to complement the consensus ranking with information about the predicted poses. The protocol was benchmarked using 15 relevant protein targets with known actives and decoys, and applied using the main protease of the SARS-CoV-2 virus. For the application, different crystal structures of the protease, and frames obtained from molecular dynamics simulations were used to dock a library of 79 molecules derived from previously co-crystallized fragments. The ranking obtained with dockECR was used to prioritize eight candidates, which were evaluated in terms of the interactions generated with key residues from the protease. The protocol can be implemented in any virtual screening campaign involving proteins as molecular targets. The dockECR code is publicly available at: https://github.com/rochoa85/dockECR.

Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases.

With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, we combine QSAR and docking methodologies to identify compounds with potential inhibitory activity of vasoactive metalloproteases for the treatment of cardiovascular diseases. To develop this study, we used a database of 191 thermolysin inhibitor compounds, which is the largest as far as we know. First, we use Dragon's molecular descriptors (0-3D) to develop classification models using Bayesian networks (Naive Bayes) and artificial neural networks (Multilayer Perceptron). The obtained models are used for virtual screening of small molecules in the international DrugBank database. Second, docking experiments are carried out for all three enzymes using the Autodock Vina program, to identify possible interactions with the active site of human metalloproteases. As a result, high-performance artificial intelligence QSAR models are obtained for training and prediction sets. These allowed the identification of 18 compounds with potential inhibitory activity and an adequate oral bioavailability profile, which were evaluated using docking. Four of them showed high binding energies for the three enzymes, and we propose them as potential dual ACE/NEP inhibitors for the control of blood pressure. In summary, the in silico strategies used here constitute an important tool for the early identification of new antihypertensive drug candidates, with substantial savings in time and money.

Exploring the molecular structures that confer ligand selectivity for galanin type II and III receptors.

Galanin receptors (GALRs) belong to the superfamily of G-protein coupled receptors. The three GALR subtypes (GALR1, GALR2, and GALR3) are activated by their endogenous ligands: spexin (SPX) and galanin (GAL). The synthetic SPX-based GALR2-specific agonist, SG2A, plays a dual role in the regulation of appetite and depression-like behaviors. Little is known, however, about the molecular interaction between GALR2 and SG2A. Using site-directed mutagenesis and domain swapping between GALR2 and GALR3, we identified residues in GALR2 that promote interaction with SG2A and residues in GALR3 that inhibit interaction with SG2A. In particular, Phe103, Phe106, and His110 in the transmembrane helix 3 (TM3) domain; Val193, Phe194, and Ser195 in the TM5 domain; and Leu273 in the extracellular loop 3 (ECL3) domain of GALR2 provide favorable interactions with the Asn5, Ala7, Phe11, and Pro13 residues of SG2A. Our results explain how SG2A achieves selective interaction with GALR2 and inhibits interaction with GALR3. The results described here can be used broadly for in silico virtual screening of small molecules for the development of GALR subtype-specific agonists and/or antagonists.

A De-Novo drug design and ADMET study to design small molecule stabilisers targeting mutant (V210I) human prion protein against familial Creutzfeldt-Jakob disease (fCJD)

The purpose of our project was to computationally design small molecule stabilisers targeting mutant (V210I) human prion protein (HuPrP) using combined De-novo pharmacophore based drug design and virtual molecular docking. The newly designed molecules were also analysed so it might qualify as a new cure for the familial Creutzfeldt-Jakob disease (fCJD). We collected the target protein structure from protein data bank (RCSB PDB). and minimised the energy using Yasara energy minimisation webserver and validated the structure using RAMPAGE webserver. We used KV Finder, a plug-in of Pymol to identify the drug binding pockets in the target protein. The pocket information was used for de-novo ligand design using the e-LEA3D webserver. Those ligands were used to generate a pharmacophore using LigandScout for the selected pockets. The pharmacophores were used as the search templates using Pharmit for the virtual screening of small molecules from Pubchem database followed by the docking of the screened small molecules in the pockets using Autodock Vina. Best five molecules were selected for ADMET properties using SwissADME. All the five small molecules were proven to be the ideal candidates for further drug development.

A De-Novo drug design and ADMET study to design small molecule stabilisers targeting mutant (V210I) human prion protein against familial Creutzfeldt-Jakob disease (fCJD)

The purpose of our project was to computationally design small molecule stabilisers targeting mutant (V210I) human prion protein (HuPrP) using combined De-novo pharmacophore based drug design and virtual molecular docking. The newly designed molecules were also analysed so it might qualify as a new cure for the familial Creutzfeldt-Jakob disease (fCJD). We collected the target protein structure from protein data bank (RCSB PDB). and minimised the energy using Yasara energy minimisation webserver and validated the structure using RAMPAGE webserver. We used KV Finder, a plug-in of Pymol to identify the drug binding pockets in the target protein. The pocket information was used for de-novo ligand design using the e-LEA3D webserver. Those ligands were used to generate a pharmacophore using LigandScout for the selected pockets. The pharmacophores were used as the search templates using Pharmit for the virtual screening of small molecules from Pubchem database followed by the docking of the screened small molecules in the pockets using Autodock Vina. Best five molecules were selected for ADMET properties using SwissADME. All the five small molecules were proven to be the ideal candidates for further drug development.

Modeling the Targeting of RNA‐Binding Proteins Using Small Molecular Inhibitors: A Novel Approach For Cancer Treatment

RNA Binding Proteins (RBPs) are proteins that bind to single or double‐stranded RNA in cells leading to the formation of ribonucleoprotein complexes. RBPs contain different structural motifs including RNA recognition motif (RRM), dsRNA‐binding domain, zinc‐finger etc. RBPs play an important role in splicing, polyadenylation, mRNA stabilization, mRNA localization, and translation. The Olathe North MSOE Center for BioMolecular Modeling SMART Team used 3‐D modeling and printing technology to examine structure‐function relationships of a RBP. To discover the interactions between proteins or modifications in RNA, cross‐linking and immunoprecipitation (CLIP) assays are used. Several techniques have been based off CLIP including photoactivatable‐ribonucleoside‐enhanced crosslinking and immunoprecipitation (PARCLIP), high‐throughput sequencing of RNA isolated by crosslinking (HTS‐CLIP), individual nucleotide resolution Cross‐Linking and Immunoprecipitation (ICLIP), etc. are also developed. RNA binding proteins have a wide variety of functions, and they are involved in many steps of cancer development and progression. Abnormal expression of RBPs is linked with the survival rate of cancer patients. RBPs role in cancer varies from protein to protein, as some proteins are overexpressed, underexpressed, or deregulated in certain cancers. When RBPs are deregulated (caused by factors such as genomic alterations, epigenetic mechanism, noncoding RNA‐mediated regulation, and PTMs), they target RNAs, which affects the characteristics of cancer cells. Hence, they serve as an attractive target for cancer drug discovery. Among RBPs, RNA‐binding protein RBM3 can bind to the AU rich sequences in the mRNA. This was shown to induce colon carcinogenesis. RBM3 has also been shown to stabilize rapidly degrading mRNAs like cyclooxygenase 2 (COX‐2), interleukin‐8 (IL‐8), and vascular endothelial growth factor (VEGF). RBM3 also increases translation on a global level by interacting with the 60S ribosome. Previous studies have shown that RBM3 acts as protooncogene in colon cancer, it induces stem‐like characteristics in colon cancer cells by increasing side population, spheroid formation and increased the expression of stem cell markers such as DCLK1 and LGR5. Recently, Dr. Anant's group found that RBM3 plays a significant role in chromatin remodeling at the DCLK1 locus thereby enhancing expression of the short form of the protein thereby increasing stem‐like phenotype in colon cancer cell lines. Targeting RBM3 using small molecular inhibitors is hypothesized to be a probable treatment for colon cancer. To predict small molecules targeting RMB3, we used homology modeling technique study to generate a 3D‐protein structure of RBMs. IDOCK and I‐TASSER servers are used to perform virtual screening of small molecules. Structural‐based drug design method is used to design small molecules that can target RBM3. These compounds will serve as lead molecules for future drug design and discovery.Support or Funding InformationSponsors: Chris Elniff and LB Fogt Mentor: Dr. Prasad Dandawate and Dr. Shrikant Anant, Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS66160.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.

This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. In contrast to the conventional persistent homology, multi-component persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for protein-ligand binding analysis and virtual screening of small molecules. Extensive numerical experiments involving 4,414 protein-ligand complexes from the PDBBind database and 128,374 ligand-target and decoy-target pairs in the DUD database are performed to test respectively the scoring power and the discriminatory power of the proposed topological learning strategies. It is demonstrated that the present topological learning outperforms other existing methods in protein-ligand binding affinity prediction and ligand-decoy discrimination.

Function and structure-based screening of compounds, peptides and proteins to identify drug candidates.

Drug discovery in simple words is all about finding small molecular compounds that possess the potential to interact with specific bio-macromolecules, mainly proteins, thereby bringing a desired effect in the functioning of the target molecules. Virtual screening of large compound libraries using computational approaches has come up as a great alternative to cost and labor-intensive high-throughput screening carried out in laboratories. Virtual high-throughput screening enormously reduces the number of compounds for systematic analysis using biochemical assays before entering the clinical trials. Here, we first give a brief overview of the rationale behind virtual screening, types of virtual screening - structure-based, ligand-based and inverse virtual screening, and challenges that need to be addressed to improve the existing strategies. Subsequently, we describe the methodology adopted for virtual screening of small molecules, peptides and proteins. Finally, we use few case studies to provide a better insight to the application of computer-aided high-throughput screening.

Identification of small molecule inhibitors of the Aurora-A/TPX2 complex.

Aurora kinases are a family of cell division regulators that govern the correct assembly of a bipolar mitotic spindle and the fidelity of chromosome segregation. Their overexpression is associated with genomic instability and aneuploidy, and is frequently observed in cancer. Accordingly, competitive inhibitors targeting Aurora kinase activity at the ATP-binding site are being investigated for therapeutic purposes. Despite promising pre-clinical data, these molecules display moderate effects in clinical trials and incomplete selectivity, either against distinct family members, or other kinases. As an alternative approach, protein-protein interaction inhibitors targeting mitotic kinases and their activators can be exploited to achieve increased specificity of action. In this study, a virtual screening of small molecules led to the identification of 25 potential inhibitors of the interaction between Aurora-A and its activator TPX2. In vitro experiments confirmed that 4 hits bind Aurora-A in the low micromolar range and compete for TPX2 binding. Immunofluorescence assays showed that 2 compounds also yield lowered Aurora-A activity and spindle pole defects in cultured osteosarcoma cells. The identified protein-protein interaction inhibitors of the Aurora-A/TPX2 complex might represent lead compounds for further development towards pioneering anti-cancer drugs and provide the proof-of-concept for a new exploitable strategy to target mitotic kinases.

The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing.

Target fishing often relies on the use of reverse docking to identify potential target proteins of ligands from protein database. The limitation of reverse docking is the accuracy of current scoring funtions used to distinguish true target from non-target proteins. Many contemporary scoring functions are designed for the virtual screening of small molecules without special optimization for reverse docking, which would be easily influenced by the properties of protein pockets, resulting in scoring bias to the proteins with certain properties. This bias would cause lots of false positives in reverse docking, interferring the identification of true targets. In this paper, we have conducted a large-scale reverse docking (5000 molecules to 100 proteins) to study the scoring bias in reverse docking by DOCK, Glide, and AutoDock Vina. And we found that there were actually some frequency hits, namely interference proteins in all three docking procedures. After analyzing the differences of pocket properties between these interference proteins and the others, we speculated that the interference proteins have larger contact area (related to the size and shape of protein pockets) with ligands (for all three docking programs) or higher hydrophobicity (for Glide), which could be the causes of scoring bias. Then we applied the score normalization method to eliminate this scoring bias, which was effective to make docking score more balanced between different proteins in the reverse docking of benchmark dataset. Later, the Astex Diver Set was utilized to validate the effect of score normalization on actual cases of reverse docking, showing that the accuracy of target prediction significantly increased by 21.5% in the reverse docking by Glide after score normalization, though there was no obvious change in the reverse docking by DOCK and AutoDock Vina. Our results demonstrate the effectiveness of score normalization to eliminate the scoring bias and improve the accuracy of target prediction in reverse docking. Moreover, the properties of protein pockets causing scoring bias to certain proteins we found here can provide the theory basis to further optimize the scoring functions of docking programs for future research.

Machine Learning Approaches Toward Building Predictive Models for Small Molecule Modulators of miRNA and Its Utility in Virtual Screening of Molecular Databases.

The ubiquitous role of microRNAs (miRNAs) in a number of pathological processes has suggested that they could act as potential drug targets. RNA-binding small molecules offer an attractive means for modulating miRNA function. The availability of bioassay data sets for a variety of biological assays and molecules in public domain provides a new opportunity toward utilizing them to create models and further utilize them for in silico virtual screening approaches to prioritize or assign potential functions for small molecules. Here, we describe a computational strategy based on machine learning for creation of predictive models from high-throughput biological screens for virtual screening of small molecules with the potential to inhibit microRNAs. Such models could be potentially used for computational prioritization of small molecules before performing high-throughput biological assay.

Drug/nondrug classification using Support Vector Machines with various feature selection strategies

In conjunction with the advance in computer technology, virtual screening of small molecules has been started to use in drug discovery. Since there are thousands of compounds in early-phase of drug discovery, a fast classification method, which can distinguish between active and inactive molecules, can be used for screening large compound collections. In this study, we used Support Vector Machines (SVM) for this type of classification task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. The data sets consist of 631 compounds for training set and 216 compounds for a separate test set. In data pre-processing step, the Pearson's correlation coefficient used as a filter to eliminate redundant features. After application of the correlation filter, a single SVM has been applied to this reduced data set. Moreover, we have investigated the performance of SVM with different feature selection strategies, including SVM–Recursive Feature Elimination, Wrapper Method and Subset Selection. All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods. We have tested SVM as a classification tool in a real-life drug discovery problem and our results revealed that it could be a useful method for classification task in early-phase of drug discovery.

Discovery of loperamide as an antagonist of angiopoietin1 and angiopoietin2 by virtual screening

The angiopoietin–Tie2 binding and related signal transduction pathways are crucial for vascular angiogenesis, blood vessel integrity and maturation. In this study, we preformed a virtual screening of small molecules targeting to Tie2. The binding site was selected at the extracellular ligand binding region of Tie2, rather than its conventional endocellular ATP binding region. It was found that loperamide, a widely-used antidiarrhea drug, was among the top hits. The binding between loperamide and Tie2 was confirmed by surface plasmon resonance (SPR) assay. Loperamide competitively inhibited the binding of both angiopoietin1 and angiopoietin2. These results indicate that loperamide is an antagonist of angiopoietin1 and angiopoietin2.

MOLA: a bootable, self-configuring system for virtual screening using AutoDock4/Vina on computer clusters

BackgroundVirtual screening of small molecules using molecular docking has become an important tool in drug discovery. However, large scale virtual screening is time demanding and usually requires dedicated computer clusters. There are a number of software tools that perform virtual screening using AutoDock4 but they require access to dedicated Linux computer clusters. Also no software is available for performing virtual screening with Vina using computer clusters. In this paper we present MOLA, an easy-to-use graphical user interface tool that automates parallel virtual screening using AutoDock4 and/or Vina in bootable non-dedicated computer clusters.ImplementationMOLA automates several tasks including: ligand preparation, parallel AutoDock4/Vina jobs distribution and result analysis. When the virtual screening project finishes, an open-office spreadsheet file opens with the ligands ranked by binding energy and distance to the active site. All results files can automatically be recorded on an USB-flash drive or on the hard-disk drive using VirtualBox. MOLA works inside a customized Live CD GNU/Linux operating system, developed by us, that bypass the original operating system installed on the computers used in the cluster. This operating system boots from a CD on the master node and then clusters other computers as slave nodes via ethernet connections.ConclusionMOLA is an ideal virtual screening tool for non-experienced users, with a limited number of multi-platform heterogeneous computers available and no access to dedicated Linux computer clusters. When a virtual screening project finishes, the computers can just be restarted to their original operating system. The originality of MOLA lies on the fact that, any platform-independent computer available can he added to the cluster, without ever using the computer hard-disk drive and without interfering with the installed operating system. With a cluster of 10 processors, and a potential maximum speed-up of 10x, the parallel algorithm of MOLA performed with a speed-up of 8,64× using AutoDock4 and 8,60× using Vina.