High-throughput Screening Experiments Research Articles

Combining Cloud-Based Free-Energy Calculations, Synthetically Aware Enumerations, and Goal-Directed Generative Machine Learning for Rapid Large-Scale Chemical Exploration and Optimization.

The hit identification process usually involves the profiling of millions to more recently billions of compounds either via traditional experimental high-throughput screens (HTS) or computational virtual high-throughput screens (vHTS). We have previously demonstrated that, by coupling reaction-based enumeration, active learning, and free energy calculations, a similarly large-scale exploration of chemical space can be extended to the hit-to-lead process. In this work, we augment that approach by coupling large scale enumeration and cloud-based free energy perturbation (FEP) profiling with goal-directed generative machine learning, which results in a higher enrichment of potent ideas compared to large scale enumeration alone, while simultaneously staying within the bounds of predefined drug-like property space. We can achieve this by building the molecular distribution for generative machine learning from the PathFinder rules-based enumeration and optimizing for a weighted sum QSAR-based multiparameter optimization function. We examine the utility of this combined approach by designing potent inhibitors of cyclin-dependent kinase 2 (CDK2) and demonstrate a coupled workflow that can (1) provide a 6.4-fold enrichment improvement in identifying <10 nM compounds over random selection and a 1.5-fold enrichment in identifying <10 nM compounds over our previous method, (2) rapidly explore relevant chemical space outside the bounds of commercial reagents, (3) use generative ML approaches to "learn" the SAR from large scale in silico enumerations and generate novel idea molecules for a flexible receptor site that are both potent and within relevant physicochemical space, and (4) produce over 3 000 000 idea molecules and run 1935 FEP simulations, identifying 69 ideas with a predicted IC50 < 10 nM and 358 ideas with a predicted IC50 < 100 nM. The reported data suggest combining both reaction-based and generative machine learning for ideation results in a higher enrichment of potent compounds over previously described approaches and has the potential to rapidly accelerate the discovery of novel chemical matter within a predefined potency and property space.

2292-PUB: Luminescent Assays Quantitate the Biology of Peptide Hormones

The interest in metabolic diseases like diabetes, nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH), continues to increase due to their growing impact on cell health. To facilitate studies and uncover novel drug targets, there is a significant need to develop disease relevant in vitro cellular models and robust, easy to use detection technologies. Here we demonstrate that luminescent assays coupling specific dehydrogenases to the production of NAD(P)H and generation of light can be used to study insulin biology and its effects on key metabolic pathways. Insulin stimulated glucose uptake was measured in 3T3-L1 MBX adipocytes with 10-fold assay window and the expected pharmacology using a glucose uptake assay. Using a glycerol detection assay, we showed 4-fold increase in lipolysis upon isoproterenol stimulation that was suppressed by insulin. Effects of insulin on gluconeogenesis was studied by measuring glucose production in liver microtissues, which are also a useful model for NAFLD studies and measurement of steatosis using a triglyceride assay. This assay does not require organic extraction and has the sensitivity required for quantitative triglyceride detection using low cell numbers (&amp;lt;1000 cells/per well). Pancreatic islets are critical for regulated glucose homeostasis and measuring hormone production and secretion is the most direct method for assessing the quality and function of islets. To facilitate those studies, we developed bioluminescent, in solution, no wash insulin and glucagon immunoassays that are significantly more rapid and cost-effective than current commonly used approaches. The assays show high sensitivity and wide dynamic range (10 pM to 8 nM for insulin and 0.5 pM to 1 nM for glucagon) and are amenable to miniaturization making them well suited for rapid measurements of large numbers of samples that are generated, for example, in perifusion or high throughput screening experiments. Disclosure J. Vidugiriene: None. D.M. Leippe: None. M. Valley: None.

Prioritizing targeted therapies in an evidence-based manner, integrating biological context and functional precision medicine.

e14065 Background: It is becoming increasingly common for cancer patients to undergo molecular profiling of their tumors in order to see whether there are any actionable DNA, gene expression, or protein expression signatures. For example, individuals with ER+ or HER2+ breast cancer or KRAS wild type (non-mutated) colorectal cancer are prescribed specific targeted therapies. When an individual’s molecular alterations do not match any currently-approved recommendations for their tumor type, their clinician may consider prescribing a therapy approved in a different tumor type. Unfortunately, tumors often eventually become resistant to the therapies they are exposed to, leading to a narrowing of options after each therapy line. Methods: We previously developed CDGnet, an evidence-based approach and web-based tool for prioritizing targeted therapies based on tumor molecular profiles based on known pathways which provide biological context. CDGnet considers approved therapies with biomarkers among the alterations for the individual’s tumor type and other tumor types as the first and second evidence level categories respectively. These are followed by therapies that target or have as biomarkers genes or proteins downstream of altered oncogenes, considering curated pathways for the individual’s tumor type and other tumor types as the third and fourth evidence level categories respectively. We are currently expanding CDGnet in order to include data from high-throughput screening (HTS) experiments of NCI oncologic drugs performed on patient-derived organoids. The concept of “functional precision medicine” consists of using functional drug efficacy determination directly on individual patients, in this case by considering drugs with low half maximal effective concentrations (EC50) which are tested on tissues derived from the actual patients. Results: We will present extensions to CDGnet that allow users to upload both the molecular profiles and the HTS data to see whether any drugs are predicted by both approaches or whether specific combinations appear promising for further testing. Preliminary results on a set of glioblastoma samples will be presented. Conclusions: We hope that extending CDGnet to also include HTS data will eventually allow a truly multi-factorial personalized oncology approach, whereby both molecular alterations at the DNA, RNA, and protein levels and patient-derived organoids will be considered in deciding on treatment plans for individuals.

Categorical Matrix Completion With Active Learning for High-Throughput Screening.

The recent advances in wet-lab automation enable high-throughput experiments to be conducted seamlessly. In particular, the exhaustive enumeration of all possible conditions is always involved in high-throughput screening. Nonetheless, such a screening strategy is hardly believed to be optimal and cost-effective. By incorporating artificial intelligence, we design an open-source model based on categorical matrix completion and active machine learning to guide high throughput screening experiments. Specifically, we narrow our scope to the high-throughput screening for chemical compound effects on diverse protein sub-cellular locations. In the proposed model, we believe that exploration is more important than the exploitation in the long-run of high-throughput screening experiment, Therefore, we design several innovations to circumvent the existing limitations. In particular, categorical matrix completion is designed to accurately impute the missing experiments while margin sampling is also implemented for uncertainty estimation. The model is systematically tested on both simulated and real data. The simulation results reflect that our model can be robust to diverse scenarios, while the real data results demonstrate the wet-lab applicability of our model for high-throughput screening experiments. Lastly, we attribute the model success to its exploration ability by revealing the related matrix ranks and distinct experiment coverage comparisons.

Schrodinger Suite Based Molecular Docking Studies of Metabolites Isolated From Endophytic Fungi for Multi-Target Therapy

In the past 30 years, incredible achievements happened in drug discovery, especially the naturally-occurring compounds play a significant role in recent drug discovery. Endophytes colonize in the inner tissues of plant species without causing observable symptoms of the disease. Those endophytes are proven to produce many structurally diverse secondary metabolites. Such molecules exhibit remarkable multitarget biological activities, including antimicrobial, anticancer, anti-inflammatory, and antiviral properties. Compare to the combinations of single-target drugs, multitarget drugs have advantages of higher efficacy and improved safety. In recent years, drug repurposing has become one of the primary methods to discover multitarget drugs. To screen multipotent therapeutic fungal metabolites, advanced computational techniques like Schrodinger Mastero suite is the leading method, without attempting a high-throughput screening and exhaustive experiments. In this study, identified fungal secondary metabolites were used for the virtual screening and molecular docking of potential multitarget drug-using academic edition of Schrodinger maestro suite.

High Thermoelectric Performance of Cu-Doped PbSe-PbS System Enabled by High-Throughput Experimental Screening.

Recent advances in high-throughput (HTP) computational power and machine learning have led to great achievements in exploration of new thermoelectric materials. However, experimental discovery and optimization of thermoelectric materials have long relied on the traditional Edisonian trial and error approach. Herein, we demonstrate that ultrahigh thermoelectric performance in a Cu-doped PbSe-PbS system can be realized by HTP experimental screening and precise property modulation. Combining the HTP experimental technique with transport model analysis, an optimal Se/S ratio showing high thermoelectric performance has been efficiently screened out. Subsequently, based on the screened Se/S ratio, the doping content of Cu has been subtly adjusted to reach the optimum carrier concentration. As a result, an outstanding peak zT~1.6 is achieved at 873 K for a 1.8 at% Cu-doped PbSe0.6S0.4 sample, which is the superior value among the n-type Te-free lead chalcogenides. We anticipate that current work will stimulate large-scale unitization of the HTP experimental technique in the thermoelectric field, which can greatly accelerate the research and development of new high-performance thermoelectric materials.

Improved method of structure-based virtual screening based on ensemble learning.

Virtual screening has become a successful alternative and complementary technique to experimental high-throughput screening technologies for drug design. Since the scoring function of docking software cannot predict binding affinity accurately, how to improve the hit rate remains a common issue in structure-based virtual screening. This paper proposed a target-specific virtual screening method based on ensemble learning named ENS-VS. In this method, protein–ligand interaction energy terms and structure vectors of the ligands were used as a combination descriptor. Support vector machine, decision tree and Fisher linear discriminant classifiers were integrated into ENS-VS for predicting the activity of the compounds. The results showed that the enrichment factor (EF) 1% of ENS-VS was 6 times higher than that of Autodock vina. Compared with the newest virtual screening method SIEVE-Score, the mean EF 1% and AUC of ENS-VS (mean EF 1% = 52.77, AUC = 0.982) were statistically significantly higher than those of SIEVE-Score (mean EF 1% = 42.64, AUC = 0.912) on DUD-E datasets; and the mean EF 1% and AUC of ENS-VS (mean EF 1% = 29.73, AUC = 0.793) were also higher than those of SIEVE-Score (mean EF 1% = 25.56, AUC = 0.765) on eight DEKOIS datasets. ENS-VS also showed significant improvements compared with other similar research. The source code is available at https://github.com/eddyblue/ENS-VS.

C. elegans protein interaction network analysis probes RNAi validated pro-longevity effect of nhr-6, a human homolog of tumor suppressor Nr4a1

Protein-protein interaction (PPI) studies are gaining momentum these days due to the plethora of various high-throughput experimental methods available for detecting PPIs. Proteins create complexes and networks by functioning in harmony with other proteins and here in silico network biology hold the promise to reveal new functionality of genes as it is very difficult and laborious to carry out experimental high-throughput genetic screens in living organisms. We demonstrate this approach by computationally screening C. elegans conserved homologs of already reported human tumor suppressor and aging associated genes. We select by this nhr-6, vab-3 and gst-23 as predicted longevity genes for RNAi screen. The RNAi results demonstrated the pro-longevity effect of these genes. Nuclear hormone receptor nhr-6 RNAi inhibition resulted in a C. elegans phenotype of 23.46% lifespan reduction. Moreover, we show that nhr-6 regulates oxidative stress resistance in worms and does not affect the feeding behavior of worms. These findings imply the potential of nhr-6 as a common therapeutic target for aging and cancer ailments, stressing the power of in silico PPI network analysis coupled with RNAi screens to describe gene function.

Subminute micro-isolation of pharmaceuticals with ultra-high pressure liquid chromatography

The drive for faster separations while maintaining quality and yield remains an important consideration for enhanced productivity as well as cost reduction for drug discovery laboratories in the pharmaceutical industry. High-throughput experimentation (HTE) and high-throughput screening (HTS) techniques can benefit from rapid and efficient isolation of product at high purity and recovery from microgram-scale crude reaction mixtures. In this study we describe the isolation of small molecule and biomolecule crude mixtures at the microgram-scale (100–2500 μg) in single or library format with methods as fast as 1.0 min and system pressures averaging 10,000 psi with an ultra-high pressure liquid chromatography (UHPLC) setup. UHPLC technology provides several advantages for rapid (<1.0 min) separations with small-particle (1.8–3.5 μm) size 4.6 × 50 mm C18 columns such as minimal extra column and delay volume, fast detector response time, and higher linear velocities for improved speed and resolution. We typically see a 5–10 fold improvement in purification time and overall sample processing time with low fraction volumes and same-day drying when compared with traditional semi-preparative techniques. There is a significant 50-fold reduction in solvent usage per run, resulting in a much lower cost of solvent and waste handling. Fluidic pathways have been optimized for collection into tared high-density 96 or 384 well 2D barcoded storage tubes in a microtiter plate (MTP) layout. Coupling the system to robotics has enabled us to implement a fully integrated automation platform with additional capabilities for small-scale purification at high speed and reduced cost of materials. The resulting arrays of small-quantity, high-purity compounds enable synthetic route scouting for HTE and HTS for biological target validation.

Quality Control of Quantitative High Throughput Screening Data.

Quantitative high throughput screening (qHTS) experiments can generate 1000s of concentration-response profiles to screen compounds for potentially adverse effects. However, potency estimates for a single compound can vary considerably in study designs incorporating multiple concentration-response profiles for each compound. We introduce an automated quality control procedure based on analysis of variance (ANOVA) to identify and filter out compounds with multiple cluster response patterns and improve potency estimation in qHTS assays. Our approach, called Cluster Analysis by Subgroups using ANOVA (CASANOVA), clusters compound-specific response patterns into statistically supported subgroups. Applying CASANOVA to 43 publicly available qHTS data sets, we found that only about 20% of compounds with response values outside of the noise band have single cluster responses. The error rates for incorrectly separating true clusters and incorrectly clumping disparate clusters were both less than 5% in extensive simulation studies. Simulation studies also showed that the bias and variance of concentration at half-maximal response (AC50) estimates were usually within 10-fold when using a weighted average approach for potency estimation. In short, CASANOVA effectively sorts out compounds with “inconsistent” response patterns and produces trustworthy AC50 values.

High-Throughput Experimental Study of Wurtzite Mn1-x Zn x O Alloys for Water Splitting Applications.

We used high-throughput experimental screening methods to unveil the physical and chemical properties of Mn1–xZnxO wurtzite alloys and identify their appropriate composition for effective water splitting application. The Mn1–xZnxO thin films were synthesized using combinatorial pulsed laser deposition, permitting for characterization of a wide range of compositions with x varying from 0 to 1. The solubility limit of ZnO in MnO was determined using the disappearing phase method from X-ray diffraction and X-ray fluorescence data and found to increase with decreasing substrate temperature due to kinetic limitations of the thin-film growth at relatively low temperature. Optical measurements indicate the strong reduction of the optical band gap down to 2.1 eV at x = 0.5 associated with the rock salt-to-wurtzite structural transition in Mn1–xZnxO alloys. Transmission electron microscopy results show evidence of a homogeneous wurtzite alloy system for a broad range of Mn1–xZnxO compositions above x = 0.4. The wurtzite Mn1–xZnxO samples with the 0.4 < x < 0.6 range were studied as anodes for photoelectrochemical water splitting, with a maximum current density of 340 μA cm–2 for 673 nm-thick films. These Mn1–xZnxO films were stable in pH = 10, showing no evidence of photocorrosion or degradation after 24 h under water oxidation conditions. Doping Mn1–xZnxO materials with Ga dramatically increases the electrical conductivity of Mn1–xZnxO up to ∼1.9 S/cm for x = 0.48, but these doped samples are not active in water splitting. Mott–Schottky and UPS/XPS measurements show that the presence of dopant atoms reduces the space charge region and increases the number of mid-gap surface states. Overall, this study demonstrates that Mn1–xZnxO alloys hold promise for photoelectrochemical water splitting, which could be enhanced with further tailoring of their electronic properties.

High-throughput computational pipeline for 3-D structure preparation and in silico protein surface property screening: A case study on HBcAg dimer structures

Knowledge-based experimental design can aid biopharmaceutical high-throughput screening (HTS) experiments needed to identify critical manufacturability parameters. Prior knowledge can be obtained via computational methods such as protein property extraction from 3-D protein structures. This study presents a high-throughput 3-D structure preparation and refinement pipeline that supports structure screenings with an automated and data-dependent workflow. As a case study, three chimeric virus-like particle (VLP) building blocks, hepatitis B core antigen (HBcAg) dimers, were constructed. Molecular dynamics (MD) refinement quality, speed, stability, and correlation to zeta potential data was evaluated using different MD simulation settings. Settings included 2 force fields (YASARA2 and AMBER03) and 2 pKa computation methods (YASARA and H++). MD simulations contained a data-dependent termination via identification of a 2 ns Window of Stability, which was also used for robust descriptor extraction. MD simulation with YASARA2, independent of pKa computation method, was found to be most stable and computationally efficient. These settings resulted in a fast refinement (6.6–37.5 h), a good structure quality (−1.17–−1.13) and a strong linear dependence between dimer surface charge and complete chimeric HBcAg VLP zeta potential. These results indicate the computational pipeline’s applicability for early-stage candidate assessment and design optimization of HTS manufacturability or formulability experiments.

Synthesis of diN-Substituted Glycyl-Phenylalanine Derivatives by Using Ugi Four Component Reaction and Their Potential as Acetylcholinesterase Inhibitors

Ugi four component reaction (Ugi-4CR) isocyanide-based multicomponent reactions were used to synthesize diN-substituted glycyl-phenylalanine (diNsGF) derivatives. All of the synthesized compounds were characterized by spectroscopic and spectrometric techniques. In order to evaluate potential biological applications, the synthesized compounds were tested in computational models that predict the bioactivity of organic molecules by using only bi-dimensional molecular information. The diNsGF derivatives were predicted as cholinesterase inhibitors. Experimentally, all of the synthesized diNsGF derivatives showed moderate inhibitory activities against acetylcholinesterase (AChE) and poor activities against butyrylcholinesterase (BuChE). Compound 7a has significant activity and selectivity against AChE, which reveals that the diNsGF scaffold could be improved to reach novel candidates by combining other chemical components of the Ugi-4CR in a high-throughput combinatorial screening experiment. Molecular docking experiments of diNsGF derivatives inside AChE suggest that these compounds placed the phenylalanine group at the peripheral site of AChE. The orientations and chemical interactions of diNsGF derivatives were analyzed, and the changeable groups were identified for future exploration of novel candidates that could lead to the improvement of diNsGF derivative inhibitory activities.

Rapid analytical characterization of high-throughput chemistry screens utilizing desorption electrospray ionization mass spectrometry

Application of high-speed DESI-MS analysis for the identification of optimal reaction conditions through high-throughput experimentation screening.

Brazilian malaria molecular targets (BraMMT): selected receptors for virtual high-throughput screening experiments.

BACKGROUND Owing to increased spending on pharmaceuticals since 2010, discussions about rising costs for the development of new medical technologies have been focused on the pharmaceutical industry. Computational techniques have been developed to reduce costs associated with new drug development. Among these techniques, virtual high-throughput screening (vHTS) can contribute to the drug discovery process by providing tools to search for new drugs with the ability to bind a specific molecular target.OBJECTIVES In this context, Brazilian malaria molecular targets (BraMMT) was generated to execute vHTS experiments on selected molecular targets of Plasmodium falciparum.METHODS In this study, 35 molecular targets of P. falciparum were built and evaluated against known antimalarial compounds.FINDINGS As a result, it could predict the correct molecular target of market drugs, such as artemisinin. In addition, our findings suggested a new pharmacological mechanism for quinine, which includes inhibition of falcipain-II and a potential new antimalarial candidate, clioquinol.MAIN CONCLUSIONS The BraMMT is available to perform vHTS experiments using OCTOPUS or Raccoon software to improve the search for new antimalarial compounds. It can be retrieved from www.drugdiscovery.com.br or download of Supplementary data.

Development and Testing of Druglike Screening Libraries.

Although significant advances in experimental high throughput screening (HTS) have been made for drug lead identification, in silico virtual screening (VS) is indispensable owing to its unique advantage over experimental HTS, target-focused, cheap, and efficient, albeit its disadvantage of producing false positive hits. For both experimental HTS and VS, the quality of screening libraries is crucial and determines the outcome of those studies. In this paper, we first reviewed the recent progress on screening library construction. We realized the urgent need for compiling high-quality screening libraries in drug discovery. Then we compiled a set of screening libraries from about 20 million druglike ZINC molecules by running fingerprint-based similarity searches against known drug molecules. Lastly, the screening libraries were objectively evaluated using 5847 external actives covering more than 2000 drug targets. The result of the assessment is very encouraging. For example, with the Tanimoto coefficient being set to 0.75, 36% of external actives were retrieved and the enrichment factor was 13. Additionally, drug target family specific screening libraries were also constructed and evaluated. The druglike screening libraries are available for download from https://mulan.pharmacy.pitt.edu .

Automated Design of Efficient and Functionally Diverse Enzyme Repertoires

Substantial improvements in enzyme activity demand multiple mutations at spatially proximal positions in the active site. Such mutations, however, often exhibit unpredictable epistatic (non-additive) effects on activity. Here we describe FuncLib, an automated method for designing multipoint mutations at enzyme active sites using phylogenetic analysis and Rosetta design calculations. We applied FuncLib to two unrelated enzymes, a phosphotriesterase and an acetyl-CoA synthetase. All designs were active, and most showed activity profiles that significantly differed from the wild-type and from one another. Several dozen designs with only 3-6 active-site mutations exhibited 10- to 4,000-fold higher efficiencies with a range of alternative substrates, including hydrolysis of the toxic organophosphate nerve agents soman and cyclosarin and synthesis of butyryl-CoA. FuncLib is implemented as a web server (http://FuncLib.weizmann.ac.il); it circumvents iterative, high-throughput experimental screens and opens the way to designing highly efficient and diverse catalytic repertoires.

BET Inhibition-Induced GSK3β Feedback Enhances Lymphoma Vulnerability to PI3K Inhibitors

The phosphatidylinositol 3 kinase (PI3K)-glycogen synthase kinase β (GSK3β) axis plays a central role in MYC-driven lymphomagenesis, and MYC targeting with bromodomain and extraterminal protein family inhibitors (BETi) is a promising treatment strategy in lymphoma. In a high-throughput combinatorial drug screening experiment, BETi enhance the antiproliferative effects of PI3K inhibitors in a panel of diffuse large B cell lymphoma (DLBCL) and Burkitt lymphoma cell lines. BETi or MYC silencing upregulates several PI3K pathway genes and induces GSK3β S9 inhibitory phosphorylation, resulting in increased β-catenin protein abundance. Furthermore, BETi or MYC silencing increases GSK3β S9 phosphorylation levels and β-catenin protein abundance through downregulating the E2 ubiquitin conjugating enzymes UBE2C and UBE2T. In a mouse xenograft DLBCL model, BETi decrease MYC, UBE2C, and UBE2T and increase phospho-GSK3β S9 levels, enhancing the anti-proliferative effect of PI3K inhibitors. Our study reveals prosurvival feedbacks induced by BETi involving GSK3β regulation, providing a mechanistic rationale for combination strategies.

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds.

Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Discovery of Mcl-1 inhibitors from integrated high throughput and virtual screening

Protein-protein interactions (PPIs) represent important and promising therapeutic targets that are associated with the regulation of various molecular pathways, particularly in cancer. Although they were once considered “undruggable,” the recent advances in screening strategies, structure-based design, and elucidating the nature of hot spots on PPI interfaces, have led to the discovery and development of successful small-molecule inhibitors. In this report, we are describing an integrated high-throughput and computational screening approach to enable the discovery of small-molecule PPI inhibitors of the anti-apoptotic protein, Mcl-1. Applying this strategy, followed by biochemical, biophysical, and biological characterization, nineteen new chemical scaffolds were discovered and validated as Mcl-1 inhibitors. A novel series of Mcl-1 inhibitors was designed and synthesized based on the identified difuryl-triazine core scaffold and structure-activity studies were undertaken to improve the binding affinity to Mcl-1. Compounds with improved in vitro binding potency demonstrated on-target activity in cell-based studies. The obtained results demonstrate that structure-based analysis complements the experimental high-throughput screening in identifying novel PPI inhibitor scaffolds and guides follow-up medicinal chemistry efforts. Furthermore, our work provides an example that can be applied to the analysis of available screening data against numerous targets in the PubChem BioAssay Database, leading to the identification of promising lead compounds, fuelling drug discovery pipelines.