Find research datasets worth reusing

opencc-by-3.0Apr 2021View details →

Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance

Dataset of the results published in the <a href="https://zenodo.org/record/4742379#.YMzExOgzYmJ">J. Mater. Chem. A, 2021, 9, 10466</a>. The files represent: i) the measured compositional and optoelectronic data of each solar cell, as well as the data generated from the Raman spectra analysis; ii) Raman spectra of the representative cells; iii) Machine Learning discriminants. The elemental composition of the different cells of the combinatorial sample was determined by X-ray fluorescence (XRF) using a Fischerscope XDV system with a 1 mm spot diameter, a 50 kV acceleration voltage, a Ni10 lter and a 45 s acquisition time. Raman analysis with blue (442 nm) and green (532 nm) excitation wavelengths were performed on the bare absorber, while measurements with NIR (785 nm) were performed in complete devices using Horiba Jobin Yvon FHR640 and iHR320 monochromators coupled with CCD detectors. The first monochromator is optimized for the UV and visible spectral ranges and was used with 442 nm (He–Cd gas laser) and 532 nm (solid state laser) excitation wavelengths. The second monochromator is optimized for the NIR range and was used with a 785 nm (solid state laser) excitation wavelength. The power density of the lasers was kept below 150 W cm2 and the spot size was ~70 \(\mu\)m. The measurements were performed in a backscattering configuration through a specific probe designed at IREC. The J–V characteristics of the devices were obtained under simulated AM1.5 illumination (1000 W m2 intensity at room temperature) using a pre-calibrated Class AAA solar simulator (Abet Technologies Sun 3000).

opencc-by-4.0Nov 2023View details →

S1Data: ChIP-seq Data from Ferrie et. al. "p300 Is an Obligate Integrator of Combinatorial Transcription Factors Inputs"

ChIP data from Ferrie et. al. "p300 Is an Obligate Integrator of Combinatorial Transcription Factors Inputs"

opencc-by-4.0Jun 2022View details →

Dataset for Dynamics of Solid-Electrolyte Interphase Formation on Silicon Electrodes Revealed by Combinatorial Electrochemical Screening

This dataset provides the raw data to the manuscript "Dynamics of Solid-Electrolyte Interphase Formation on Silicon Electrodes Revealed by Combinatorial Electrochemical Screening" published in Angewandte Chemie International Edition (2022): <a href="https://doi.org/10.1002/anie.202207184">https://doi.org/10.1002/anie.202207184</a> Specifically, the following measurements are provided: <ul> <li>Electrochemical measurements for combinatorial preparation of solid-electrolyte layers under different conditions and repetitions ("SECCM/")</li> <li>Raman spectra obtained using SHINERS for all the prepared conditions and repetitions ("SHINERS/")</li> <li>Atomic force microscopy data for each SEI layer ("AFM/")</li> <li>Energy-dispersive X-ray spectroscopy data ("EDX/")</li> </ul>

opencc-by-4.0Jun 2024View details →

Computational Design of Multimodal Combinatorial Mechanical Metamaterials

This dataset contains the data used to design multimodal mechanical metamaterials as described in the paper 'Prospecting for Pluripotency in Metamaterial Design', as published in Phys. Rev. Research 7(2), 023299. In this paper, the data is used to design 5×5 unit cells with desired deformation (zero) modes. The dataset contains the data used to train neural networks (CNN_data.zip), the designs generated by genetic algorithm (step_i.zip) and their mode structures (step_ii.zip), and the designs obtained through our design approach as described in the paper (step_ii.zip). Additionally, there is data comparing the efficiency of using a genetic algorithm or a hill climbing method to generate designs with a large number of intensive modes (step_i.zip).

opencc-by-4.0Nov 2024View details →

Data for "Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems"

Contains instances of combinatorial optimizations problems (Sherrington-Kirkpatrick and MAX 2-SAT) as well as further results and plotting notebooks for the paper "Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems".

opencc-by-4.0Jul 2023View details →

Data for GECCO2023 Paper "Many-objective (Combinatorial) Optimization is Easy"

Data for Paper "Many-objective (Combinatorial) Optimization is Easy" <ul> <li>instances.tar.xz contains 𝜌mnk-landscape instances</li> <li>metrics.csv contains the metric-values based on full enumeration</li> <li>performance.csv contains the Pareto resolution and the hypervolume of the different algorithms one each instance</li> <li>performance_neval.csv contains the number of evaluations performed by PLS</li> <li>script.R is the R script for producing the figures</li> </ul> Reference Arnaud Liefooghe and Manuel López-Ibáñez. 2023. Many-objective (Combinatorial) Optimization is Easy. In Genetic and Evolutionary Computation Conference (GECCO '23), July 15–19, 2023, Lisbon, Portugal. <a href="https://doi.org/10.1145/3583131.3590475">https://doi.org/10.1145/3583131.3590475</a> Abstract It is a common held assumption that problems with many objectives are harder to optimize than problems with two or three objectives. In this paper, we challenge this assumption and provide empirical evidence that increasing the number of objectives tends to reduce the difficulty of the landscape being optimized. Of course, increasing the number of objectives brings about other challenges, such as an increase in the computational effort of many operations, or the memory requirements for storing non-dominated solutions. More precisely, we consider a broad range of multi- and many-objective combinatorial benchmark problems, and we measure how the number of objectives impacts the dominance relation among solutions, the connectedness of the Pareto set, and the landscape multimodality in terms of local optimal solutions and sets. Our analysis shows the limit behavior of various landscape features when adding more objectives to a problem. Our conclusions do not contradict previous observations about the inability of Pareto-optimality to drive search, but we explain these observations from a different perspective. Our findings have important implications for the design and analysis of many-objective optimization algorithms.

opencc-by-4.0May 2023View details →

Supplementary codes and datasets for "Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials"

This repository provides data and codes for manuscript “Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials”  by M. Do&scaron;kář, M. Somr, R. Hlůžek, J. Havelka, J. Novák, and J. Zeman, published first as a preprint <a href="https://arxiv.org/abs/2305.09280">arXiv:2305.09280</a> at arXiv.org; see the actual description of the Zenodo entry for the latest reference. This repository contains: <ol> <li>MATLAB and C++ source codes for combinatorial design and numerical analyses (folder <code>./numerics/</code>),</li> <li>experimental data (folder <code>./experiments/</code>),</li> <li>3D models of parts used in robotic-assembly (folder <code>./models/</code>),</li> <li>a control script for robotic assembly (folder <code>./robotics/</code>).</li> </ol> Numerics All simulations were performed with an in-house MATLAB code, which extends the finite element toolbox for finite strain calculations accompanying the work of <a href="https://doi.org/10.1016/j.cma.2020.113333">van Bree, S. E. H. M., Roko&scaron;, O., Peerlings, R. H. J., Do&scaron;kář, M., & Geers, M. G. D. (2020). A Newton solver for micromorphic computational homogenization enabling multiscale buckling analysis of pattern-transforming metamaterials. Computer Methods in Applied Mechanics and Engineering, 372, 113333</a>. In particular, this snapshot corresponds to a cleaned-up version (excluding files unrelated to the publications) of commit <code>21cfc2e9</code>. The MATLAB codebase contains MEX files written in C++ to accelerate selected procedures. In order to run any code, these MEX files must be compiled first. We use CMake build automation, with the main <code>CMakeLists.txt</code> located in <code>./numerics/mex</code>. Combinatorial search was performed by the <code>RUN_modular_exploration.m</code> script; see definition of problems with the script. The results of the enumerations, stored in <code>./dat/exploration</code>, were analysed with <code>POST_modular_S_v3.m</code>, identifying layouts leading to the extreme (min/max) tilt angles. Comparison against experimental measurements was facilitated by a series of scripts <code>POST_DIC_{...}.m</code>. First, run <code>POST_DIC_step1_extract_points_in_mesh.m</code> to identify locations.mat. Next, post-process extensometer data with <code>POST_DIC_step2_merge_extensometer_data.m</code>, and use <code>POST_DIC_step3_impose_extracted_BC.m</code> to parse DIC results in a format suitable for imposing BC later in this script. Finally, comparison between experimental and computed displacements is provided by <code>POST_DIC_step4_modular_comparison_experiments.m</code>. (Note that the particular files need to be manually provided in the “Compute deformation process” part of <code>POST_DIC_step4_modular_comparison_experiments.m</code>.) Experimental data This folder contains data from (i) an unixaial tension test of a dogbone specimen (both from a MTS loading machine and DIC data) and (ii) two measurement sessions extracting the L-shape domain responses using DIC (<code>20_11_30 - Hluzek_Elka_newassemblyplan</code> and <code>21_04_12 - Hluzek_ Elka_quarters</code> with lower loading threshold). For post-processing, see the above-mentioned <code>POST_DIC_{...}.m</code> scripts. <code>*.mat</code> files present directly in <code>./experiments/</code> folder were obtained and are need by those scripts. 3D models The folder contains geometrical models for individual parts needed for robot-assisted assembly of module molds for casting. This includes: <ol> <li>a silo extension to store more tiles (file <code>silo_extension.stl</code>),</li> <li>formwork modules around the main structure for the purpose of casting silicone (file <code>tile_formwork.stl</code>),</li> <li>all types of tiles for the inside structure (file <code>tile_inside_types.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_1.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_2.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_3.stl</code>),</li> <li>connection for spacers (file <code>yumi_base_4.stl</code>),</li> <li>spacer holding a silo and the build plate (file <code>yumi_base_5.stl</code>),</li> <li>YuMi grippers with extensions to hold the tiles (file <code>grippers_extend.st</code>).</li> </ol> Robotics The folder contains a single file with a script created in RobotStudio (RobotWare Version: 6.08.01.00, SmartGripper Version: 3.55.0000.00) to assemble the plan with YuMi IRB 14000-0.5/0.5 left hand. Acknowledgement The related research, experiments, and code development were supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.

opencc-by-4.0Aug 2022View details →

Comparative Analysis of Anthraquinone and Chalcone Derivatives-Based Virtual Combinatorial Library. A Cheminformatics "Proof-of-Concept" Study

This computational “proof-of-concept” study illustrated the combinatorial approach used to explain how the selected natural products' structures undergo molecular diversity analysis. A virtual combinatorial library (1.6M) based on 20 anthraquinones and 24 chalcones were enumerated. The resulting compounds were optimized to the near drug-likeness properties and the physicochemical descriptors were calculated for all datasets including FDA, Non-FDA, and natural products (NPs) datasets from ZINC 15. UMAP and principal component analysis (PCA) were applied to compare and represent the chemical space coverage of each dataset. Subsequently, the Laplacian score, and Gini coefficient, were applied to delineate feature selection, and selectivity among properties respectively. Finally, we demonstrated the diversity between the datasets by employing Murcko’s, and central scaffolds systems, calculated three fingerprint descriptors, and analyzed their diversity by PCA and self-organizing maps (SOM). The optimized enumeration resulted in 1,610,268 compounds with NP-Likeness, and synthetic feasibility mean scores close to FDA, Non-FDA, and NPs datasets. The overlap between the chemical space of 1.6M was more prominent with NPs. Laplacian score has prioritized NP-likeness and hydrogen bond acceptor properties (1.0 and 0.923) respectively, while the Gini coefficient showed that all properties have selective effects on datasets (0.81 to 0.93). Scaffold and fingerprint diversity indicated that the descending order for the tested datasets was FDA, Non-FDA, NPs, 1.6M. Virtual combinatorial libraries based on NPs can be considered as a source of the combinatorial compound with NP-likeness properties. Furthermore, measuring molecular diversity is supposed to be performed by different methods to allow for comparison and better judgment.  This link provides an illustration of the whole virtual combinatorial library using the TMAP algorithm in addition to the complete dataset. TMAP is a recent algorithm applied to visualize ultra-large high-dimensional chemical libraries for structures and physicochemical properties (Probst & Reymond, 2020). This approach creates and distributes intuitive tree representations of big data sets with arbitrary dimensionality in the order of 107. To visualize the whole library of compounds, download the "index(2).rar", then extract the index.html that pop-up in the WinRAR application.

opencc-by-4.0Nov 2024View details →

Dataset - A Python-Based Approach to Sputter Deposition Simulations in Combinatorial Materials Science

This dataset accompanies the publication "A Python-Based Approach to Sputter Deposition Simulations in Combinatorial Materials Science," which presents and validates pySIMTRA, a Python wrapper for the Monte Carlo-based SIMTRA simulation tool. The dataset includes all measured and simulated data shown in the publication, as well as additional animations visualizing the compositions in the multinary composition space. The dataset contains the compositions for each of the seven materials libraries (in at.%) in the quaternary Ni-Pd-Pt-Ru system. Additionally, it provides the simulated number of particles as outputted by SIMTRA, which serve as the basis for composition estimation. Both the compositional data and the particle counts are supplied in .csv format. To supplement the results, 3D animations of the quaternary compositional spaces are included, showing the comparison between simulated compositions (red dots) and measured compositions (blue dots). These animations offer a more intuitive visualization of the data compared to the static Figures in the publication and are supplied as .gif files. Due to the in-depth analysis of cathode tilt discussed in the paper, the dataset also includes simulation results for the ternary Pd-Pt-Ru library, highlighting the effect of varying the cathode tilt angle. Simulations were conducted for tilt angles of 10°, 9.5°, 9°, and 8.5°.

opencc-by-4.0Apr 2024View details →

Dataset for High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating

This is a dataset for the manuscript High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating. This mansucript is currently under peer-review in ACS Nano.

opencc-by-4.0Mar 2018View details →

Data Set Used in Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS

This document contains the data set used for the study Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS that is currently in submission.

opencc-by-4.0Sep 2023View details →

Repository for: Combinatorial Wnt signaling landscape during brachiopod anteroposterior patterning

This repository contains the data and analyses for the manuscript: Vellutini, B. C., Martín-Durán, J. M., Børve, A. & Hejnol, A. Combinatorial Wnt signaling landscape during brachiopod anteroposterior patterning. BMC Biol. 22, 1–23 (2024). <a href="https://doi.org/10.1186/s12915-024-01988-w" rel="nofollow">https://doi.org/10.1186/s12915-024-01988-w</a> The source is maintained at <a href="https://github.com/bruvellu/terebratalia-wnts">https://github.com/bruvellu/terebratalia-wnts</a>.

openmit-licenseMar 2024View details →

KinFragLib: Combinatorial library

KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination. Project description. Protein kinases play a crucial role in many cell signaling processes, making them one of the most important families of drug targets. In this context, fragment-based drug design strategies have been successfully applied to develop novel kinase inhibitors, usually following a knowledge-driven approach to optimize a focused set of fragments to a potent kinase inhibitor. Alternatively, KinFragLib is a new method that allows to explore and extend the chemical space of kinase inhibitors using data-driven fragmentation and recombination, built on available structural kinome data from the KLIFS database for over 3,200 kinase DFG-in complexes. The computational fragmentation method splits the co-crystallized non-covalent kinase inhibitors into fragments with respect to their 3D proximity to six predefined functionally relevant subpocket centers. The resulting fragment library consists of six subpocket pools with over 9,000 fragments, available at <a href="https://github.com/volkamerlab/KinFragLib">https://github.com/volkamerlab/KinFragLib</a>. KinFragLib offers two main applications: (i) In-depth analyses of the chemical space of known kinase inhibitors, subpocket characteristics and connections, as well as (ii) subpocket-informed recombination of fragments to generate potential novel inhibitors. The latter showed that recombining only a subset of 722 representative fragments generated a combinatorial library of 11.3 million molecules, containing, besides some known kinase inhibitors, more than 99% novel chemical matter compared to ChEMBL and 56% molecules compliant with Lipinski's rule of five. Combinatorial library dataset. The dataset offered here is part of the KinFragLib GitHub repository (<a href="https://github.com/volkamerlab/KinFragLib">https://github.com/volkamerlab/KinFragLib</a>) and contains the metadata and properties of the KinFragLib combinatorial library. 1. Raw data <ul> <li>combinatorial_library.json: Full combinatorial library, please refer to notebooks/4_1_combinatorial_library_data_preparation.ipynb at <a href="https://github.com/volkamerlab/KinFragLib">https://github.com/volkamerlab/KinFragLib</a> for detailed information about this data format.</li> <li>combinatorial_library_deduplicated.json: Deduplicated combinatorial library (based on InChIs).</li> <li>chembl_standardized_inchi.csv: Standardized ChEMBL 36 molecules in the form of InChI strings.</li> <li>klifs_download_summary.csv: PDB codes of all KLIFS structures used to generate the KinFragLib fragmentation library. </li> </ul> 2. Processed data Data extracted from combinatorial_library_deduplicated.json, performed in notebooks/4_1_combinatorial_library_data_preparation.ipynb at <a href="https://github.com/volkamerlab/KinFragLib">https://github.com/volkamerlab/KinFragLib</a>. <ul> <li>n_atoms.csv: Number of atoms for each recombined ligand.</li> <li>ro5.csv: Number of ligands that fulfill Lipinski's rule of five (Ro5) and its individual criteria; number of ligands in total.</li> <li>subpockets.csv: Number of ligands per subpocket combination.</li> <li>original_exact.json: Ligands with exact matches in original ligands, i.e. KLIFS ligands that were used for the fragmentation.</li> <li>original_substructure.json: Ligands with substructure matches in original ligands, i.e. KLIFS ligands that were used for the fragmentation.</li> <li>chembl_exact.json: Ligands with exact matches in ChEMBL.</li> <li>chembl_most_similar.json: Most similar ligand in ChEMBL for each recombined ligand.</li> <li>chembl_highly_similar.json: Most similar ligand in ChEMBL for each recombined ligand with similarity greater than 0.9.</li> </ul> Usage. This dataset can be used to run the notebooks available on <a href="https://github.com/volkamerlab/KinFragLib">https://github.com/volkamerlab/KinFragLib</a>. <ol> <li>Clone the KinFragLib repository.</li> <li>Download the tar.bz2 file provided here.</li> <li>Extract the archive content to the combinatorial library folder in your local KinFragLib folder and run the notebooks.</li> </ol> <pre><code>tar -xvf combinatorial_library.tar.bz2 -C /path_to_kinfraglib/data/combinatorial_library/</code></pre>  Citation. This dataset is part of the KinFragLib publication: Sydow, D., Schmiel, P., Mortier, J., and Volkamer, A. KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination. J. Chem. Inf. Model. 2020. <a href="https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c00839">https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c00839</a>

opencc-by-4.0Dec 2022View details →

Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty

We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function. The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the robust knapsack problem. Both problem sets have been described and used for benchmarking in the paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty", published in Mathematical Programming Computation by Christina Büsing, Timo Gersing and Arie Koster. Furthermore, we provide instances for robust weighted matching on bipartite graphs and robust weighted independent set. The latter are based on graphs for the clique problem of the second DIMACS implementation challenge (1993). Both problem sets have been described and used for benchmarking in the paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty", presented at IPCO 2023 by the same authors.   Paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/s12532-022-00232-2"> https://doi.org/10.1007/s12532-022-00232-2</a> Paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/978-3-031-32726-1_5">https://doi.org/10.1007/978-3-031-32726-1_5</a> For algorithms solving these problems see: <a href="https://doi.org/10.5281/zenodo.7463371">https://doi.org/10.5281/zenodo.7463371</a>

opencc-zeroMay 2020View details →

Data from: Combinatorial signal processing in an insect

Human language is combinatorial: phonemes are grouped into syllables, syllables into words, and so on. The capacity for combinatorial processing is present to different degrees in some mammals and birds. We tested for basic combinatorial processing in an insect against two competing hypotheses: beginning rule (where the early signal portions play a stronger role in acceptability); and no rule (where the order of signal elements plays no role in signal acceptability). We worked with Enchenopa treehoppers, whose vibrational signals consist of a whine (W) followed by pulses (P). The combinatorial rule hypothesis predicts females will prefer any stimuli containing the natural-combination (WP or PWP) over reverse-order stimuli (PW). The beginning rule hypothesis predicts that females will prefer stimuli with natural beginnings (WP or W) over stimuli with modified beginnings (PW or PWP). The no rules hypothesis predicts no preferences in stimuli acceptability. In playback experiments using laser vibrometry, females preferred natural-combination signals regardless of the beginning element (WP or PWP) and discriminated against reverse-order signals (PW) or individual elements (W or P). Finding support for the combinatorial rule hypothesis in insects suggests that this capability represents a common solution to the problems presented by complex communication.

opencc-zeroAug 2020View details →

The relationship between a combinatorial processing rule and a continuous mate preference function in an insect

Mate choice involves processing signals that can reach high levels of complexity and feature multiple components, even in small animals with tiny brains. This raises the question of whether and how such organisms deal with this complexity. One solution involves combinatorial processing, whereby different signal elements are processed as single units. Combinatorial processing has been described in several mammals and birds, and recently in a vibrationally signalling insect, Enchenopa treehoppers. Here, we ask about the relationship between combinatorial rules and mate preferences for continuously varying signal features. Enchenopa male advertisement signals are composed of two elements: a 'whine' followed by a set of pulses. The dominant frequency of the whine and element combination both matter to females. We presented synthetic signals varying in element order (natural [whine-pulses], reverse [pulses-whine]) and in frequency to Enchenopa females and recorded their responses. The reverse combination resulted in a decrease in attractiveness of the signals, and also slightly changed the shape of the preference for frequency. We found that females could be classified into three 'types': females with both a strong preference and a strong combinatorial rule, females with both a weak preference and weak rule and females with a strong preference but a weak rule. Our results suggest that in Enchenopa signal processing, the mate preference for a continuous signal feature 'takes precedence' over, but also interacts with, the combinatorial rule. The relationship between the preference and the rule could evolve to take different forms according to selection on mate choice decisions. We suggest that exploring the relationship between such preferences and rules in species with more complex signals will bring insight into the evolution of the multi-component communication systems.

opencc-zeroDec 2020View details →

COVID-CDR: An integrative resource for network-based investigation of COVID-19 combinatorial drug repositioning and mechanism of action

Repurposing of the existing medications has become the mainstream focus of anti-COVID-19 drug discovery as it offers rapid and cost-effective solutions for therapeutic development. However, a great effort is still required to enhance the efficacy of repurposing therapeutic options through combination therapy, in which promising drugs with varying mechanisms of action are administered together. Nonetheless, our ability to identify and validate effective combinations is limited due to the vast number of possible drug pairs. Yet, there is no available resource that can systematically guide to identify or choose the effective individual drugs or best possible synergistic drug combinations for the treatment of SARS-CoV-2 infection. To address this resource gap, we developed a web-based platform that displays the network-based mechanism of action of drug combinations, thus simultaneously providing a visual of the cellular interactome involved in modes of action of the chosen drugs. The platform allows the freedom to select two or more drug combinations and provides the options to investigate the network-based efficacy of drug combinations and understand the similarity score, primary indications, and pairwise synergistic scores on different cell-lines. In a nutshell, the platform (accessible via <a href="http://vafaeelab.com/COVID19_repositioning.html">http://vafaeelab.com/COVID19_repositioning.html</a>) is the first of its type, which provides a systematic approach for pre-clinical investigation of combination therapy for treating COVID-19 at the fingertips of the clinicians or researchers.