Large-scale Virtual Screening Research Articles

Relative assessment of different statistical instruments and measures for the prediction of promising outcomes using docking, virtual screening and ADMET analysis against HIV-RT

Reverse transcriptase is the most therapeutic target for the discovery of novel, potent, and non-toxic new anti-retroviral drugs. In the present work, various docking software such as Sybyl Surflex-Dock, OpenEye FRED, and Hermes GOLD were evaluated for their efficiency to reproduce known cognate inhibitors' conformations. Three metrics were used and compared to assess the performance of the applied scoring functions, i.e. enrichment factor, receiver operating characteristic (ROC) curves, and Bedroc analysis. Twelve different scoring functions of three softwares were used to assess their ability to rank the cognate ligand within the active site of its proteins. The extensive virtual screening task was performed on eight crystal structures, and the performance of docking and scoring was assessed by their ability to efficiently detect known active compounds enriched in the top-ranked of the list among a randomly selected dataset of the ten thousand compounds of the NCI database. The effectiveness of post-docking relaxation in Surflex was also evaluated. The top 20, 50, and 100 compounds were selected based on consensus scoring functions from all 48 proteins with different ligand complexes. Further, the shortlisted leads were subjected to ADMET via using Discovery Studio. The results further implicate the importance of various statistical tools that should be followed before large-scale virtual screening for the drug discovery process. In silico results demonstrating the experiment was successful. The study of the research covers the combinatorial in silico techniques such as benchmarking of the softwares and scoring functions, statistical tools applied for screening and different conformations of HIV-RT crystal structures for virtual screening with rigid and flexible molecular docking and molecular dynamics simulation approach. This study reveals a clear roadmap to identify novel scaffolds against HIV-RT for antiretroviral therapy, thus providing the remedial solutions of HIV related infections and other diseases caused by malfunctioning of the target protein. Communicated by Ramaswamy H. Sarma

A novel ligand of the translationally controlled tumor protein (TCTP) identified by virtual drug screening for cancer differentiation therapy

SummaryIntroduction Differentiation therapy is a promising strategy for cancer treatment. The translationally controlled tumor protein (TCTP) is an encouraging target in this context. By now, this field of research is still at its infancy, which motivated us to perform a large-scale screening for the identification of novel ligands of TCTP. We studied the binding mode and the effect of TCTP blockade on the cell cycle in different cancer cell lines. Methods Based on the ZINC-database, we performed virtual screening of 2,556,750 compounds to analyze the binding of small molecules to TCTP. The in silico results were confirmed by microscale thermophoresis. The effect of the new ligand molecules was investigated on cancer cell survival, flow cytometric cell cycle analysis and protein expression by Western blotting and co-immunoprecipitation in MOLT-4, MDA-MB-231, SK-OV-3 and MCF-7 cells. Results Large-scale virtual screening by PyRx combined with molecular docking by AutoDock4 revealed five candidate compounds. By microscale thermophoresis, ZINC10157406 (6-(4-fluorophenyl)-2-[(8-methoxy-4-methyl-2-quinazolinyl)amino]-4(3H)-pyrimidinone) was identified as TCTP ligand with a KD of 0.87 ± 0.38. ZINC10157406 revealed growth inhibitory effects and caused G0/G1 cell cycle arrest in MOLT-4, SK-OV-3 and MCF-7 cells. ZINC10157406 (2 × IC50) downregulated TCTP expression by 86.70 ± 0.44% and upregulated p53 expression by 177.60 ± 12.46%. We validated ZINC10157406 binding to the p53 interaction site of TCTP and replacing p53 by co-immunoprecipitation. Discussion ZINC10157406 was identified as potent ligand of TCTP by in silico and in vitro methods. The compound bound to TCTP with a considerably higher affinity compared to artesunate as known TCTP inhibitor. We were able to demonstrate the effect of TCTP blockade at the p53 binding site, i.e. expression of TCTP decreased, whereas p53 expression increased. This effect was accompanied by a dose-dependent decrease of CDK2, CDK4, CDK, cyclin D1 and cyclin D3 causing a G0/G1 cell cycle arrest in MOLT-4, SK-OV-3 and MCF-7 cells. Our findings are supposed to stimulate further research on TCTP-specific small molecules for differentiation therapy in oncology.

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.

Polyphenols as alternative treatments of COVID-19

Although scientists around the world have put lots of effort into the development of new treatments for COVID-19 since the outbreak, no drugs except Veklury (remdesivir) have been approved by FDA. There is an urgent need to discover some alternative antiviral treatment for COVID-19. Because polyphenols have been shown to possess antiviral activities, here we conducted a large-scale virtual screening for more than 400 polyphenols. Several lead compounds such as Petunidin 3-O-(6″-p-coumaroyl-glucoside) were identified to have promising binding affinities and convincing binding mechanisms. Analyzing the docking results and ADME properties sheds light on the potential efficacy of the top-ranked drug candidates and pinpoints the key residues on the target proteins for the future of drug development.

Computational generation of an annotated gigalibrary of synthesizable, composite peptidic macrocycles

Peptidomimetic macrocycles have the potential to regulate challenging therapeutic targets. Structures of this type having precise shapes and drug-like character are particularly coveted, but are relatively difficult to synthesize. Our laboratory has developed robust methods that integrate small-peptide units into designed scaffolds. These methods create macrocycles and embed condensed heterocycles to diversify outcomes and improve pharmacological properties. The hypothetical scope of the methodology is vast and far outpaces the capacity of our experimental format. We now describe a computational rendering of our methodology that creates an in silico three-dimensional library of composite peptidic macrocycles. Our open-source platform, CPMG (Composite Peptide Macrocycle Generator), has algorithmically generated a library of 2,020,794,198 macrocycles that can result from the multistep reaction sequences we have developed. Structures are generated based on predicted site reactivity and filtered on the basis of physical and three-dimensional properties to identify maximally diverse compounds for prioritization. For conformational analyses, we also introduce ConfBuster++, an RDKit port of the open-source software ConfBuster, which allows facile integration with CPMG and ready parallelization for better scalability. Our approach deeply probes ligand space accessible via our synthetic methodology and provides a resource for large-scale virtual screening.

A molecular generative model of ADAM10 inhibitors by using GRU-based deep neural network and transfer learning

Recurrent neural network (RNN) is one of the most representative architectures in deep learning and widely adopted in many research fields especially in natural language processing. In this work, a gated-recurrent-unit (GRU)-based deep neural network combined with transfer learning was successfully employed to establish a molecular generative model of ADAM10 inhibitors. The results showed that the GRU-based generative model can learn accurately the SMILES grammars of the molecules and be capable of generating novel potential ADAM10 inhibitors. In comparison with traditional ligand-based methods, the GRU-based generative model requires only the SMILES information of the chemical ligands and can generate efficiently a large set of potential novel structures. These unique advantages make it extremely useful in de novo drug design and large-scale virtual screening researches.

HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy.

PURPOSEHLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures.METHODSHere we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other commonly used tools, such as MODELLER and MHCflurry, this environment includes support for diverse tasks in structural modeling, analysis, and visualization.RESULTSTo illustrate the capabilities of HLA-Arena, we describe 3 example workflows applied to peptide-HLA complexes. Leveraging the strengths of our tools, DINC and APE-Gen, the first 2 workflows show how to perform geometry prediction for peptide-HLA complexes and structure-based binding prediction, respectively. The third workflow presents an example of large-scale virtual screening of peptides for multiple HLA alleles.CONCLUSIONThese workflows illustrate the potential benefits of HLA-Arena for the structural modeling and analysis of peptide-HLA complexes. Because HLA-Arena can easily be integrated within larger computational pipelines, we expect its potential impact to vastly increase. For instance, it could be used to conduct structural analyses for personalized cancer immunotherapy, neoantigen discovery, or vaccine development.

Novel Histone Deacetylase Inhibitors and HIV-1 Latency-Reversing Agents Identified by Large-Scale Virtual Screening.

Current antiretroviral therapies used for HIV management do not target latent viral reservoirs in humans. The experimental “shock-and-kill” therapeutic approach involves use of latency-reversal agents (LRAs) that reactivate HIV expression in reservoir-containing cells, followed by infected cell elimination through viral or host immune cytopathic effects. Several LRAs that function as histone deacetylase (HDAC) inhibitors are reported to reverse HIV latency in cells and in clinical trials; however, none to date have consistently reduced viral reservoirs in humans, prompting a need to identify new LRAs. Toward this goal, we describe here a virtual screening (VS) approach which uses 14 reported HDAC inhibitors to probe PubChem and identifies 60 LRA candidates. We then show that four screening “hits” including (S)-N-Hydroxy-4-(3-methyl-2-phenylbutanamido)benzamide (compound 15), N-(4-Aminophenyl)heptanamide (16), N-[4-(Heptanoylamino)phenyl]heptanamide (17), and 4-(1,3-Dioxo-1H-benzo[de]isoquinolin-2(3H)-yl)-N-(2-hydroxyethyl)butanamide (18) inhibit HDAC activity and/or reverse HIV latency in vitro. This study demonstrates and supports that VS-based approaches can readily identify novel HDAC inhibitors and LRAs, which in turn may help toward inhibitor design and chemical optimization efforts for improved HIV shock-and-kill-based efforts.

Abstract 14: Computer-aided discovery of small-molecule inhibitors targeting neural transcription factor BRN2 in neuroendocrine prostate tumors

Abstract Therapies targeting tumor-fueling androgens have been mainstay treatments of advanced prostate cancer for almost 5 decades. While the androgen receptor (AR) inhibitor enzalutamide (ENZ) prolongs survival of castration-resistant prostate cancer patients (CRPC), ENZ-resistant (ENZR) tumors rapidly recur. Our laboratory has identified that ENZ resistance can occur both in the presence and absence of continued classical AR activity. Lack of AR activity is clinically relevant, as it has been estimated that up to 25% of men who die from advanced CRPC have non-AR driven disease and lethal AR inactive neuroendocrine and anaplastic phenotypes of CRPC emerge after hormone therapy. Recently our group identified the neural transcription factor BRN2 as a major clinically relevant driver of neuroendocrine prostate cancer (NEPC). Hence, using the integrated power of computational drug discovery platform and biologic testing, we identified first-in-field inhibitors for BRN2. First, a homology model of BRN2 protein was generated and used as an input to perform large-scale virtual screening and virtual hits were tested in a series of experiments. Cpd 18-14, a lead BRN2 inhibitor, exhibited profound inhibition of BRN2 in transcriptional assay and binding to the BRN2 in cellular and in vitro assays. Furthermore, Cpd18-14 reduced the expression of neuroendocrine genes SOX2 and NCAM1. Importantly, Cpd18-14 displayed antiproliferative activity specifically in patient-derived BRN2hi NCI-H660 and in house-developed 42DENZRcells while displaying no effect on BRN2low 16DCRPC and LNCaP cells. Further, the most stable binding orientation of Cpd18-14 was determined using molecular dynamics simulations and was used to carry out structure-based lead optimization. As a result, we developed several synthetic derivatives with significant improvement in antiproliferative activity and metabolic stability. Finally, Cpd18-94 reduced the growth of NCI-H660 and 42DENZR NEPC tumors significantly in xenograft models. We anticipate that the developed drug prototypes will lay a foundation for the development of small-molecule therapies capable of combating highly aggressive and lethal form of neuroendocrine tumors. Citation Format: Ravi Munuganti, Daksh Thaper, Adeleke Aguda, Soojin Kim, Olena Sivak, Amina Zoubeidi. Computer-aided discovery of small-molecule inhibitors targeting neural transcription factor BRN2 in neuroendocrine prostate tumors [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 14.

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov.

A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein–ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, d-Sorbitol, d-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.Electronic supplementary materialThe online version of this article (10.1007/s12539-020-00376-6) contains supplementary material, which is available to authorized users.

Virtual Screening: Is Bigger Always Better? Or Can Small Be Beautiful?

This viewpoint is intended to counterbalance some recent publications describing large-scale virtual screening by illustrating how success in launching drug discovery projects has been achieved with much more modest resources. Two examples of small-scale virtual screening that led to the discovery of clinical candidates are cited in favor of this argument.

GM-DockZn: a geometry matching-based docking algorithm for zinc proteins.

Molecular docking is a widely used technique for large-scale virtual screening of the interactions between small-molecule ligands and their target proteins. However, docking methods often perform poorly for metalloproteins due to additional complexity from the three-way interactions among amino-acid residues, metal ions and ligands. This is a significant problem because zinc proteins alone comprise about 10% of all available protein structures in the protein databank. Here, we developed GM-DockZn that is dedicated for ligand docking to zinc proteins. Unlike the existing docking methods developed specifically for zinc proteins, GM-DockZn samples ligand conformations directly using a geometric grid around the ideal zinc-coordination positions of seven discovered coordination motifs, which were found from the survey of known zinc proteins complexed with a single ligand. GM-DockZn has the best performance in sampling near-native poses with correct coordination atoms and numbers within the top 50 and top 10 predictions when compared to several state-of-the-art techniques. This is true not only for a non-redundant dataset of zinc proteins but also for a homolog set of different ligand and zinc-coordination systems for the same zinc proteins. Similar superior performance of GM-DockZn for near-native-pose sampling was also observed for docking to apo-structures and cross-docking between different ligand complex structures of the same protein. The highest success rate for sampling nearest near-native poses within top 5 and top 1 was achieved by combining GM-DockZn for conformational sampling with GOLD for ranking. The proposed geometry-based sampling technique will be useful for ligand docking to other metalloproteins. GM-DockZn is freely available at www.qmclab.com/ for academic users. Supplementary data are available at Bioinformatics online.

Large-Scale Ligand-Based Virtual Screening for SARS-CoV-2 Inhibitors Using Deep Neural Networks

Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized ChemAI, a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. We provide these top-ranked compounds as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai.

Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor Type.

By using an ensemble-docking strategy, we undertook a large-scale virtual screening campaign in order to identify new putative hits against the MET kinase target. Following a large molecular dynamics sampling of its conformational space, a set of 45 conformers of the kinase was retained as docking targets to take into account the flexibility of the binding site moieties. Our screening funnel started from about 80,000 chemical compounds to be tested in silico for their potential affinities towards the kinase binding site. The top 100 molecules selected—thanks to the molecular docking results—were further analyzed for their interactions, and 25 of the most promising ligands were tested for their ability to inhibit MET activity in cells. F0514-4011 compound was the most efficient and impaired this scattering response to HGF (Hepatocyte Growth Factor) with an IC of 7.2 M. Interestingly, careful docking analysis of this molecule with MET suggests a possible conformation halfway between classical type-I and type-II MET inhibitors, with an additional region of interaction. This compound could therefore be an innovative seed to be repositioned from its initial antiviral purpose towards the field of MET inhibitors. Altogether, these results validate our ensemble docking strategy as a cost-effective functional method for drug development.

Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network.

Identifying drug-disease associations is helpful for not only predicting new drug indications and recognizing lead compounds, but also preventing, diagnosing, treating diseases. Traditional experimental methods are time consuming, laborious and expensive. Therefore, it is urgent to develop computational method for predicting potential drug-disease associations on a large scale. Herein, a novel method was proposed to identify drug-disease associations based on the deep learning technique. Molecular structure and clinical symptom information were used to characterize drugs and diseases. Then, a novel two-dimensional matrix was constructed and mapped to a gray-scale image for representing drug-disease association. Finally, deep convolution neural network was introduced to build model for identifying potential drug-disease associations. The performance of current method was evaluated based on the training set and test set, and accuracies of 89.90 and 86.51% were obtained. Prediction ability for recognizing new drug indications, lead compounds and true drug-disease associations was also investigated and verified by performing various experiments. Additionally, 3,620,516 potential drug-disease associations were identified and some of them were further validated through docking modeling. It is anticipated that the proposed method may be a powerful large scale virtual screening tool for drug research and development. The source code of MATLAB is freely available on request from the authors.

Research on Identity Mechanism of Large-scale Drug Virtual Screening in Heterogeneous Multicore Environment and SMP Environment

This paper studies the mechanism of ultra-large-scale drug virtual screening in the heterogeneous multicore environment and SMP (Symmetric Multi-Processor) environment. Given the differences caused by different CPUs, we solved the incompatibility of the open-source molecular docking software Autodock Vina on two supercomputer systems in different environments and realized the automatic and intelligent large-scale virtual screening process for small-molecule ligands. At the same time, we propose a processing method of large-scale virtual screening using Autodock Vina in two environments, which makes full use of the dominant computing power of supercomputers to improve the efficiency of large-scale molecular docking and reduce the load of different supercomputer systems facing large-scale molecular docking tasks. Finally, we conduct a virtual screening experiment on the X86 platform supercomputer and the Sunway TaihuLight Supercomputer based on the heterogeneous multicore architecture CPU—SW26010. The experimental results show that our large-scale drug virtual screening method for different CPU environments is effective.

Computer-Aided Discovery of Small Molecule Inhibitors of Thymocyte Selection-Associated High Mobility Group Box Protein (TOX) as Potential Therapeutics for Cutaneous T-Cell Lymphomas.

Cutaneous T-cell lymphomas (CTCL) are the most common primary lymphomas of the skin. We have previously identified thymocyte selection-associated high mobility group (HMG) box protein (TOX) as a promising drug target in CTCL; however, there are currently no small molecules able to directly inhibit TOX. We aimed to address this unmet opportunity by developing anti-TOX therapeutics with the use of computer-aided drug discovery methods. The available NMR-resolved structure of the TOX protein was used to model its DNA-binding HMG-box domain. To investigate the druggability of the corresponding protein–DNA interface on TOX, we performed a pilot virtual screening of 200,000 small molecules using in silico docking and identified ‘hot spots’ for drug-binding on the HMG-box domain. We then performed a large-scale virtual screening of 7.6 million drug-like compounds that were available from the ZINC15 database. As a result, a total of 140 top candidate compounds were selected for subsequent in vitro validation. Of those, 18 small molecules have been characterized as selective TOX inhibitors.

PepVis: An integrated peptide virtual screening pipeline for ensemble and flexible docking protocols.

Peptide therapeutics is proven to be highly potential in the treatment of various diseases due to its specificity, biological safety, and cost-effectiveness. Many of the FDA-approved peptides are currently available for therapeutic applications. In the current postgenomic era, high-throughput computational screening of drugs and peptides are highly exploited in peptide therapeutics for cost-effective and robustness. However, there is a paucity of efficient pipelines that automate virtual screening process of peptides through integration of open-source tools that are optimal to perform ensemble and flexible docking protocols. Hence, in this study, we developed a GUI-based pipeline named PepVis for automated script generation for large-scale peptide modeling and virtual screening. PepVis integrates Modpep and Gromacs for peptide structure modeling and optimization; AutoDock Vina, ZDOCK, and AutoDock CrankPep for virtual screening of peptides; ZRANK2 for rescoring of protein-peptide complexes, and FlexPepDock for flexible refinement of protein-peptide complexes. Benchmarking of ensemble docking through PepVis infers that ModPep+Vina to outperform ModPep+ZDock in terms of detecting near-natives from LEADS-PEP dataset. PepVis is built modular to incorporate many other docking algorithms in the future. This pipeline is distributed freely under the GNU GPL license and can be downloaded at https://github.com/inpacdb/PepVis.

Discovery of Immunoproteasome Inhibitors Using Large-Scale Covalent Virtual Screening.

Large-scale virtual screening of boronic acid derivatives was performed to identify nonpeptidic covalent inhibitors of the β5i subunit of the immunoproteasome. A hierarchical virtual screening cascade including noncovalent and covalent docking steps was applied to a virtual library of over 104,000 compounds. Then, 32 virtual hits were selected, out of which five were experimentally confirmed. Biophysical and biochemical tests showed micromolar binding affinity and time-dependent inhibitory potency for two compounds. These results validate the computational protocol that allows the screening of large compound collections. One of the lead-like boronic acid derivatives identified as a covalent immunoproteasome inhibitor is a suitable starting point for chemical optimization.

Computer-aided drug discovery of Myc-Max inhibitors as potential therapeutics for prostate cancer

While Myc is an essential regulator of growth in normal cells, it is also frequently associated with cancer progression, therapy-resistance and lethal outcomes in most human cancers. In prostate cancer (PCa), Myc transcription factors are implicated in the pathogenesis and progression of the full spectrum of PCa, from adenocarcinoma to advanced castration-resistant and neuroendocrine phenotypes. Though a high-value therapeutic target, clinically approved anti-Myc drugs have yet to be discovered. To elicit its oncogenic effects, Myc must form a heterodimer with its partner Max, which together bind DNA and activate transcription of a spectrum of target genes that promote cell growth, proliferation, metabolism, and apoptosis while blocking differentiation. In this study, we identified a binding site on the DNA-binding domain of the structurally ordered Myc-Max complex and employed a computer-aided rational drug discovery approach to identify small molecules that effectively inhibit Myc-Max functionality. A large-scale virtual screening protocol implementing structure-based methodologies was utilized to select a set of top-ranked compounds that were subsequently evaluated experimentally and characterized mechanistically for their ability to inhibit Myc-Max transcriptional activity and subsequent downstream functions, to reduce viability in PCa cell lines, disrupt protein-DNA interactions and to induce apoptosis as their mechanism of action. Among compounds identified that effectively inhibit Myc-Max activity with low to mid-micromolar range potency and no or minimal generic cytotoxicity, VPC-70067, a close analog of the previously identified Myc inhibitor 10058-F4, served as proof-of-concept that our in silico drug discovery strategy performed as expected. Compound VPC-70063, of a chemically different scaffold, was the best performer in a panel of in vitro assays, and the forerunner for future hit-to-lead optimization efforts. These findings lay a foundation for developing more potent, specific and clinically optimized Myc-Max inhibitors that may serve as promising therapeutics, alone or in combination with current anti-cancer treatments, for treatment of specific phenotypes or heterogeneous tumors.