High-throughput Computational Screening Research Articles

Discovery of novel compounds as potent activators of Sirt3

Among the sirtuin enzymes, Sirt3 is one of the most important deacetylases as it regulates acetylation levels in mitochondria, which are linked to the metabolism of multiple organs and therefore involved in many types of age-related human diseases such as cancer, heart diseases and metabolic diseases. Given the dearth of direct activators of Sirt3, the identification of new modulators could be a key step in the development of new therapeutics. Here we report the discovery of Sirt3 modulators, including activators, through the use of DNA encoded library technology (DEL) and computational high-throughput screening methodologies. Top hits from both screenings against Sirt3 were evaluated according to their activity and affinity. Our best activator is more potent than the previously reported activator Honokiol. Docking studies suggest that our activators identified from virtual screening interact with Sirt3 at a site similar to Honokiol, whereas the activators identified from DEL selection interact with Sirt3 at an atypical site. Our results establish the attractiveness of these high-throughput screening technologies in identifying novel and potent Sirt3 activators and, therefore, in associated therapeutic applications.

Discovery of highly radon-selective metal-organic frameworks through high-throughput computational screening and experimental validation

A highly radon (Rn)-selective adsorbent is essential for the capture of radon from indoor air. Here, an aluminum-based metal–organic framework (MOF), Al-NDC, was identified as a highly Rn-selective adsorbent via high-throughput screening of 4,951 MOFs in the Computation-Ready, Experimental (CoRE) MOF database. Notably, Al-NDC experimentally demonstrated an excellent Rn removal rate (52 %) – more than twice that of the activated carbon benchmarks (25 %) – while also exhibiting excellent hydrothermal, chemical, and radioactive stabilities. Moreover, useful structure–property relationships were obtained from large-scale simulations. High crystal densities, low surface areas, small pore volumes, and small diameters of the largest cavity were found to favor the selective capture of radon. Interestingly, channel-like pores of a size appropriate to fit one to two radon molecules (4.9–9.8 Å) were found to be most effective for selective radon capture. These findings provide key insights for the future development of Rn adsorbents.

2D solar cell with record high power conversion efficiency based on low-symmetry IV-V2 bilayer heterostructure

The structural, electronic, and optical properties of two-dimensional (2D) low-symmetry orthorhombic IV-V2 bilayer heterostructures (HSs) were investigated using first-principles calculations. Our data show that all the constructed bilayer HSs (SiAs2/GeAs2, SiAs2/SiP2 and SiP2/GeAs2) are stabilized by van der Waals interaction. The IV-V2 bilayer HSs exhibit strong visible light absorption and type-II band alignments, which are beneficial for the effective separation of photo-generated electron–hole pairs in solar cells. Notably, the 2D SiAs2/GeAs2 HS-based solar cell exhibits a record-high power conversion efficiency (23.98%) higher than those of previously-reported 2D HSs-based solar cells obtained by high-throughput computational screening, owing to its small conduction band offset (0.2 eV) and suitable bandgap (1.49 eV). Our results provide insights into the structures and electronic and photovoltaic properties of the IV-V2 bilayer HSs and may be useful for related 2D solar cell applications.

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2-Acylpyrrole Derivatives.

In this work, the SOFT.PTML tool has been used to pre-process a ChEMBL dataset of pre-clinical assays of antileishmanial compound candidates. A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the IFPTML-LOGR model presents excellent values of specificity and sensitivity (81-98%) in training and validation series. The use of this software has been illustrated with a practical case study focused on a series of 28 derivatives of 2-acylpyrroles 5a,b, obtained through a Pd(II)-catalyzed C-H radical acylation of pyrroles. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated finding that compounds 5bc (IC50 = 30.87 μM, SI > 10.17) and 5bd (IC50 = 16.87 μM, SI > 10.67) were approximately 6-fold more selective than the drug of reference (miltefosine) in in vitro assays against L. amazonensis promastigotes. In addition, most of the compounds showed low cytotoxicity, CC50 > 100 μg/mL in J774 cells. Interestingly, the IFPMTL-LOGR model predicts correctly the relative biological activity of these series of acylpyrroles. A computational high-throughput screening (cHTS) study of 2-acylpyrroles 5a,b has been performed calculating >20,700 activity scores vs a large space of 647 assays involving multiple Leishmania species, cell lines, and potential target proteins. Overall, the study demonstrates that the SOFT.PTML all-in-one strategy is useful to obtain IFPTML models in a friendly interface making the work easier and faster than before. The present work also points to 2-acylpyrroles as new lead compounds worthy of further optimization as antileishmanial hits.

High-Throughput Selection and Experimental Realization of Two New Ce-Based Nitride Perovskites: CeMoN3 and CeWN3.

Nitride perovskites have only been experimentally realized in very few cases despite the widespread existence and commercial importance of perovskite materials. From oxide perovskites used in ultrasonics to halide perovskites that have revolutionized the photovoltaics industry, the discovery of new perovskite materials has historically impacted a wide number of fields. Here, we add two new perovskites, CeWN3 and CeMoN3, to the list of experimentally realized perovskite nitrides using high-throughput computational screening and subsequent high-throughput thin film growth techniques. Candidate compositions are first down-selected using a tolerance factor and then thermochemical stability. A novel competing fluorite-family phase is identified for both material systems, which we hypothesize is a transient intermediate phase that crystallizes during the evolution from an amorphous material to a stable perovskite. Different processing routes to overcome the competing fluorite phase and obtain phase-pure nitride perovskites are demonstrated for the CeMoN3–x and CeWN3–x material systems, which provide a starting point for the development of future nitride perovskites. Additionally, we find that these new perovskite phases have interesting low-temperature magnetic behavior: CeMoN3–x orders antiferromagnetically below TN ≈ 8 K with indications of strong magnetic frustration, while CeWN3–x exhibits no long-range order down to T = 2 K but has strong antiferromagnetic correlations. This work demonstrates the importance and effectiveness of using high-throughput techniques, both computational and experimental: they are integral to optimize the process of realizing two entirely novel nitride perovskites.

High-throughput computational screening of Covalent−Organic framework membranes for helium purification

Currently, helium purification is carried out by the heat-driven cryogenic distillation process, which is prohibitively energy-intensive and capital-intensive. As an alternative, covalent-organic frameworks (COFs)-based membrane separation technology is considered to be cost-efficient and technically feasible. In this study, a high-throughput computational screening strategy was proposed for the rapid determination of high-performing COF membranes for this particular application. A geometric analysis-based prescreening process was carried out on 688 experimentally synthesized COFs, resulting in 665 COF candidates with pore limited diameter (PLD) no less than 3.8 Å. Subsequently, molecular simulations were performed to estimate the henry coefficients and self-diffusion coefficients of N2, H2, and CH4 in each COF. Based on the results, the membrane selectivity and permeability of all the COFs were calculated, from which 23 high-performing COF membranes were identified. Additionally, the performances of these 23 COF membranes for a ternary mixture of He/N2/CH4 were computationally examined, five of which were highly preferable to permeate He over N2 or CH4. The results indicated that a large number of COF membranes exceeded the performance upper limit of polymer membranes owing to their high He permeability. Additionally, the relation between the PLD of COFs and the corresponding separation performances was investigated. Finally, He permeability, as well as He/CH4 and He/N2 selectivity of mixed matrix membranes were estimated by using the Maxwell model. The results revealed that the addition of COFs into pure polymers significantly improved the permeability of He up to 11689 Barrer.

Molecular-fingerprint machine-learning-assisted design and prediction for high-performance MOFs for capture of NMHCs from air

The capture of trace amounts of non-methane hydrocarbons (NMHCs) from air due to the toxicity of volatile organic compounds is a significant challenge. A total of 31399 hydrophobic metal–organic frameworks (MOFs) were first screened from 137953 hypothetical MOFs using high-throughput computational screening (HTCS), and their performance indices (adsorption capacity and selectivity) for the adsorption of NMHCs (C 3 –C 6 ) were obtained by molecular simulations. The discovery of a “second peak” near twice the kinetic diameter of the corresponding NMHC provided more choices for excellent MOFs that adsorb NMHCs. Four machine learning (ML) classification and regression algorithms predicted the performance of MOFs, and the relative importance values of the six descriptors were determined. The combination of the Random Forests algorithm and Molecular ACCess Systems molecular fingerprint (MF) had an excellent predictive ability for MOFs. According to the performance, the fingerprint commonalities of the 100 top-performing MOFs were counted, and the excellent bits (EBs) that could promote the performance were defined. Finally, new substructures containing all of the EBs were designed for each NMHC to build a new MOF database. This work combined the HTCS, ML, and MF to provide a detailed insight into the design of efficient MOFs for adsorbing NMHCs.

In silico identification and in vitro antiviral validation of potential inhibitors against Chikungunya virus.

The Chikungunya virus (CHIKV) has become endemic in the Africa, Asia and Indian subcontinent, with its continuous re-emergence causing a significant public health crisis. The unavailability of specific antivirals and vaccines against the virus has highlighted an urgent need for novel therapeutics. In the present study, we have identified small molecule inhibitors targeting the envelope proteins of the CHIKV to interfere with the fusion process, eventually inhibiting the cell entry of the virus particles. We employed high throughput computational screening of large datasets against two different binding sites in the E1-E2 dimer to identify potential candidate inhibitors. Among them, four high affinity inhibitors were selected to confirm their anti-CHIKV activity in the in vitro assay. Quercetin derivatives, Taxifolin and Rutin, binds to the E1-E2 dimer at different sites and display inhibition of CHIKV infection with EC50 values 3.6μM and 87.67μM, respectively. Another potential inhibitor with ID ChemDiv 8015-3006 binds at both the target sites and shows anti-CHIKV activity at EC50 = 41μM. The results show dose-dependent inhibitory effects of Taxifolin, Rutin and ChemDiv 8015-3006 against the CHIKV with minimal cytotoxicity. In addition, molecular dynamics studies revealed the structural stability of these inhibitors at their respective binding sites in the E1-E2 protein. In conclusion, our study reports Taxifolin, Rutin and ChemDiv 8015-3006 as potential inhibitors of the CHIKV entry. Also, this study suggests a few potential candidate inhibitors which could serve as a template to design envelope protein specific CHIKV entry inhibitors.

2D Monolayers for Superior Transparent Electromagnetic Interference Shielding.

With countless modern technologies utilizing wireless communication, materials that can selectively allow transmission of visible light and prevent transmission of low frequency GHz electromagnetic interference (EMI) are needed. Recently, 2D materials such as graphene, transition metal dichalcogenides, and MXenes have shown promise for such applications. Despite the rapid advances, little progress has been made in identifying 2D monolayers with intrinsically higher visible transmittance (Tvis) and shielding effectiveness (SE). With endless variations in structure and composition, the 2D materials space is too large for systematic experimental investigation. To tackle this challenge, we perform a high-throughput computational screening. Using an atomistic first-principles method, we simultaneously calculate Tvis and SE of 7000 2D monolayer materials. We identify 26 monolayer materials with excellent properties of >98% Tvis and >5 dB SE (∼70% EMI attenuation). The top candidate, an AgSe2 monolayer with predicted 98.53% Tvis and 12.53 dB SE (∼94% EMI attenuation), is a significant improvement over the state-of-the-art, graphene, with 96.7% Tvis and 3.04 dB SE (∼40% EMI attenuation). Additionally, we gain physical insights into the transparent EMI shielding performance of 2D monolayers and their electronic structure, elucidating the role of surface terminations and nearly free electron states.

Identifying Key Descriptors for the Single-Atom Catalyzed CO Oxidation

Identifying Key Descriptors for the Single-Atom Catalyzed CO Oxidation

Theory-guided design of hydrogen-bonded cobaltoporphyrin frameworks for highly selective electrochemical H2O2 production in acid

The pursuit of selective two-electron oxygen reduction reaction to H2O2 in acids is demanding and largely hampered by the lack of efficient non-precious-metal-based electrocatalysts. Metal macrocycles hold promise, but have been relatively underexplored. Efforts are called for to promote their inherent catalytic activities and/or increase the surface exposure of active sites. In this contribution, we perform the high-throughput computational screening of thirty-two different metalloporphyrins by comparing their adsorption free energies towards key reaction intermediates. Cobalt porphyrin is revealed to be the optimal candidate with a theoretical overpotential as small as 40 mV. Guided by the computational predictions, we prepare hydrogen-bonded cobaltoporphyrin frameworks in order to promote the solution accessibility of catalytically active sites for H2O2 production in acids. The product features an onset potential at ~0.68 V, H2O2 selectivity of >90%, turnover frequency of 10.9 s−1 at 0.55 V and stability of ~30 h, the combination of which clearly renders it stand out from existing competitors for this challenging reaction.

High-throughput discovery of fluoride-ion conductors via a decoupled, dynamic, and iterative (DDI) framework

AbstractFluoride–ion batteries are a promising alternative to lithium–ion batteries with higher theoretical capacities and working voltages, but they have experienced limited success due to the poor ionic conductivities of known electrolytes and electrodes. Here, we report a high-throughput computational screening of 9747 fluoride-containing materials in search of fluoride-ion conductors. Via a combination of empirical, lightweight DFT, and nudged elastic band (NEB) calculations, we identified >10 crystal systems with high fluoride mobility. We applied a search strategy where calculations are performed in any order (decoupled), computational resources are reassigned based on need (dynamic), and predictive models are repeatedly updated (iterative). Unlike hierarchical searches, our decoupled, dynamic, and iterative framework (DDI) began by calculating high-quality barrier heights for fluoride-ion mobility in a large and diverse group of materials. This high-quality dataset provided a benchmark against which a rapid calculation method could be refined. This accurate method was then used to measure the barrier heights for 6797 fluoride–ion pathways. The final dataset has allowed us to discover many fascinating, high-performance conductors and to derive the design rules that govern their performance. These materials will accelerate experimental research into fluoride–ion batteries, while the design rules will provide an improved foundation for understanding ionic conduction.

Machine-Learning-Assisted High-Throughput computational screening of Metal–Organic framework membranes for hydrogen separation

The high-throughput computational screening (HTCS) and machine learning (ML) were applied to evaluate the H2 separation performances of computation-ready, experimental metal–organic framework membranes (CoRE-MOFMs). For the separation of H2/X (X = CH4, N2, H2S, O2, CO2, and He), based on the results of the structure-performance relationships by univariate analysis found that the performance of the top-performing candidates far exceeded Robeson’s upper bound. To evaluate the H2 permeability (PH2) and permselectivity, a new trade-off variable, the trade-off multiple selectivity and permeability (TMSP), was defined as the comprehensive performance of the CoRE-MOFMs. The TMSP could effectively evaluate the separation performance of CoRE-MOFMs for a variety of H2/X pairs at inifinite dilution. Eight ML methods based on supervised learning algorithms were employed to predict PH2 and TMSP, and the Gaussian process regression and random forest methods exhibited the first and second highest predictive abilities, respectively. Calculating the feature importance of each CoRE-MOFM to investigate the relationship between the number of features and the accuracy of the ML model. And a decision tree model was established following the principle of the minimum Gini coefficient, which would be accurately classified the CoRE-MOFMs based on the membrane structural descriptors; the optimal structural descriptors of MOF and the physical properties of the gas to be separated are used to establish a multivariate regression model which can predict the value of the optimal structural descriptors of MOFMs by knowing the physical properties of component X in the other H2/X separation. The quantitative structure-performance relationships on the microscopic scale could provide a theoretical basis for the screening of top-performing CoRE-MOFMs. The HTCS and ML methods could efficiently accelerate the design and the development of top-performing CoRE-MOFMs for H2 separation.

Multi-scale Computer-aided molecular design of Ionic liquid for absorption heat transformer based on Machine learning

Absorption cycles have attracted considerable attention for utilizing renewable energy and waste heat to achieve carbon neutrality. Absorption Heat Transformers (AHTs) using Ionic Liquids (ILs) as absorbents show great potential in avoiding crystallization and corrosion problems. To screen the optimum candidate for AHT systems, a multi-scale computational screening method by Computer-aid Molecular Design (CAMD) is applied, and its feasibility is verified with high accuracies. For 18 typical operating conditions, the optimum IL molecules are identified from 26,360 candidates generated by the CAMD method. The relationship between the AHT cycle performance and IL molecular structures is systematically analyzed to clarify the significant IL structural factors that affect the Coefficient of Performance (COP) primarily. The correlation analysis shows that IL molecules containing main-cations ([C1N], [C1P], or [C1S]), anions ([Br], [Cl], [Lac], or [OAC]), and sub-cations with short alkyls have the highest potential to improve the cycle performance. The optimum ILs screened by the multi-scale CAMD method perform better than the currently investigated ILs, even outperforming LiBr in some specific unfavorable conditions. At an absorber temperature of 120 °C, heat source temperature of 75 °C, and condenser temperature of 25 °C, [C,1,1,1,1,OHN][OAC] yields a COP of 0.458, much higher than LiBr (0.419). By distinguishing the applicable and inapplicable operating conditions, the comparison shows that the applicable operating ranges of the selected ILs are wider than that of LiBr due to their crystallization-free nature. To accelerate the molecular design process in various operating conditions, eight Machine Learning (ML) algorithms are integrated to predict the cycle performance based on molecular descriptors with less computation cost. To verify the accuracy of the proposed multi-scale CAMD approach, experimental results of [C1,1,2,2,OHN][Br], one of the best-performing ILs, are compared with the simulation results of the multi-scale CAMD. The agreement between experiment and simulation illustrates the high accuracy of this screening method. The multi-scale CAMD approach achieves the high-throughput computational screening of optimum ILs for high-efficiency absorption systems.

Identification of novel mycobacterium tuberculosis leucyl-tRNA synthetase inhibitor using a knowledge-based computational screening approach

ObjectivesTuberculosis is a chronic lung disease caused by Mycobacterium tuberculosis (MTB), whose thick cell envelope and drug metabolizing enzymes offering it multidrug resistance. Therefore, there is a need to identify new molecular targets, biologically active as well as clinically safe anti-MTB compounds from natural resources. MethodsIn this study, we performed high throughput computational screening of FDA listed natural bioactive compounds for identifying novel anti-MTB leucyl-tRNA (LeuRS) synthetase inhibitors. ResultsInitial virtual molecular docking of 136 bioactive compounds has identified Docetaxel, Reserpine and Irinotecan as promising lead molecules owing to their structural plasticity and binding affinity with the active site of MTB-LeuRS. Further, deep molecular docking and molecular dynamics (MD) simulation analysis (at 100 ns) of the above three test compounds along with Oxaborole compound (GSK656) has demonstrated the superior binding affinity and stability of Irinotecan in forming molecular complexes with LeuRS protein. Interestingly, it also showed comparable binding residues and affinity parameters (like flexibility, structural divergence) as the GSK656 inhibitor in binding the tRNASyn domain 2 of LeuRS. A good correlation of pharmacokinetic properties (ADME-Tox) like bioavailability, absorption, solubility, and low toxicity between Irinotecan and GSK656 was evident. Competitive binding of Irinotecan to tRNASyn domain 2 of LeuRS is likely to make it unavailable to bind Leucine amino acid, which may negatively impact the protein biosynthesis and eventually inhibit the bacterial growth and attenuate the pathogen’s virulence. ConclusionsOur findings pave the way for further experimental confirmation of Irinotecan in the quest for a novel anti-LeuRS specific inhibitor to combat drug resistant MTB infection.

High-Throughput CALPHAD: A Powerful Tool Towards Accelerated Metallurgy

Introduction of high entropy alloys or multi-principal element alloys around 15 years ago motivated revising conventional alloy design strategies and proposed new ways for alloy development. Despite significant research since then, the potential for new material discoveries using the MPEA concept has hardly been scratched. Given the number of available elements and the vastness of possible composition combinations, an unlimited number of alloys are waiting to be investigated! Discovering novel high-performance materials can be like finding a needle in a haystack, which demands an enormous amount of time and computational capacity. To overcome the challenge, a systematic approach is essential to meet the growing demand for developing novel high-performance or multifunctional materials. This article aims to briefly review the challenges, recent progress and gaps, and future outlook in accelerated alloy development, with a specific focus on computational high-throughput (HT) screening methods integrated with the Calculation of Phase Diagrams (CALPHAD) technique.

Computational analysis of the optical response of ZnSe with d-orbital defects

The doping of wide band-gap semiconducting ZnSe by transition metal (TM) atoms finds applications from mid-infrared lasing, sensing, photoelectrochemical cells, to nonlinear optics. Yet understanding the response of these materials at the atomic and electronic level is lacking, particularly in comparing a range of TM dopants, which were studied primarily by phenomenological crystal-field theory. In this work, to investigate bulk ZnSe singly doped with first-row TM atoms, specifically Ti through Cu, we applied a first-principles approach and crystal-field theory to explain the origin of the infrared absorption. We show that the use of an appropriate exchange–correlation functional and a Hubbard U correction to account for electron correlation improved the determination of the electronic transitions in these systems. We outline an approach for the calculation of the crystal-field splitting from first-principles and find it useful in providing a measure of dopant effects, also in qualitative comparison to our experimental characterization for ZnSe doped with Fe, Cr, and Ni. Our calculated absorption spectra indicate absorption signatures in the mid-infrared range, while the absorption in the visible portion of the spectrum is attributed to the ZnSe host. Our calculations will potentially motivate further experimental exploration of TM-doped ZnSe. Finally, the methods used here provide a route towards computational high-throughput screening of TM dopants in III–V materials through a combination of the electronic band structure and crystal-field theory.

High-Throughput computational screening of Single-atom embedded in defective BN nanotube for electrocatalytic nitrogen fixation

The boron nitride nanotubes (BNNTs) have been widely used in various energy applications such as hydrogen storage, gas sensor, carbon dioxide reduction reaction, and so on. Herein, the potential application of BNNT-based catalysts was explored in electrocatalytic nitrogen reduction reaction (eNRR) by density functional theory (DFT) computations. A series of single metal/non-metal atoms (18 transition metals (3d ∼ 5d) and 6 non-metals (B, C, N, Si, P, and As)) supported on the defective BNNT were investigated. Among them, Os-doped (Os@BNNT) single-atom catalyst (SAC) can significantly promote the NRR process, and possess a rather low overpotential (0.31 V), as well as high selectivity over HER competition. The confined Os atom not only serves as an electron donor to supply electrons for NRR, but also acts as a channel for interfacial charge transfer between substrate and NRR intermediates. Moreover, the curved surface of BNNT can decrease the barrier for protonating *NH2 to·NH3. The synergistic collaboration of single-atom Os catalyst and curved surface of BNNT facilitates the NRR process. Our findings open up a new avenue for designing novel nanotube-based materials as eNRR electrocatalysts.

In silico and in vitro evaluation of silibinin: a promising anti-Chikungunya agent.

Chikungunya virus (CHIKV) infection and subsequent high patient morbidity is a global threat. The present study aimed to identify the potent antiviral agent against Chikungunya virus, with minimum in vitro cytotoxicity. CHIKV nsP4 3D structure was determined using the I-TASSER server followed by its refinement and pocket determination. Furthermore, high-throughput molecular docking was employed to identify candidate CHIKV nsP4 inhibitors in a library containing 214 compounds. The top ranked compound was evaluated further with various assays, including cytotoxicity, antiviral activity, time of drug addition, viral entry attachment, and microneutralization assays. High-throughput computational screening indicated silibinin to have the best interaction with CHIKV nsP4 protein, immature and mature glycoproteins with highest negative free binding energy, - 5.24 to - 5.86kcal/mol, and the lowest inhibitory constant, 50.47 to 143.2µM. Further in vitro analysis demonstrated silibinin could exhibit statistically significant (p < 0.05) dose-dependent anti-CHIKV activity within 12.5-100-µM concentrations with CC50 as 50.90µM. In total, 50µM silibinin interfered with both CHIKV attachment (75%) and entry (82%) to Vero cells. Time of addition assay revealed silibinin interfered with late phase of the CHIKV replication cycle. Microneutralization assay revealed that silibinin could inhibit clearing of 50% Vero cell monolayer caused by CHIKV-induced CPE at a minimum dose of 25µM. These data indicated silibinin to be a promising candidate drug against CHIKV infection.

In silico identification of small molecule protein-protein interaction inhibitors: targeting hotspot regions at the interface of MXRA8 and CHIKV envelope protein

Chikungunya virus (CHIKV) is an arthritogenic arbovirus responsible for re-emerging epidemics of Chikungunya fever around the world for centuries. Chikungunya has become endemic in Africa, Southeast Asia, the Indian subcontinent, and subtropical regions of the Americas. The unavailability of antiviral therapy or vaccine against the CHIKV and its continuous re-emergence demands an urgent need to develop potential candidate therapeutics. CHIKV entry into the host cell is mediated by its envelope proteins engaging the cellular receptor MXRA8 to invade the susceptible cells. We report here two essential target binding sites at the CHIKV E1-E2 proteins by identifying hotspot regions at the E1-E2-MXRA8 binding interface. Further, we employed high throughput computational screening to identify potential small molecule protein-protein interaction (PPI) modulators which could effectively bind at the identified target sites. Molecular dynamics simulations and binding free energy calculations confirmed the stability of three compounds, viz., ZINC299817498, ZINC584908978, and LAS52155651, at both the predicted interface binding sites. The polar and charged residues at the interface were responsible for energetically holding the ligands at the binding sites. Altogether, our findings suggest that the predicted target binding sites at the E1-E2 dimer could be essential to block the receptor interaction as well as the fusion process of the CHIKV particles. Thus, we identified a few small molecule PPI inhibitors with great potential to block the E1-E2-MXRA8 interaction and act as promising templates to design anti-CHIKV drugs. Communicated by Ramaswamy H. Sarma