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Dataset results
28 results for “de novo design”
Molecular datasets from "SMILES-Based Deep Generative Scaffold Decorator for De-Novo Drug Design"
<p>Herein find the molecular datasets from "<a href="https://chemrxiv.org/articles/SMILES-Based_Deep_Generative_Scaffold_Decorator_for_De-Novo_Drug_Design/11638383">SMILES-Based Deep Generative Scaffold Decorator for De-Novo Drug Design</a>". These were generated with SMILES-based scaffold decorator generative models trained with two training sets (DRD2 and ChEMBL). These generative models require a partially-built molecule (scaffold) as input and output several possible completions for each scaffold. Each dataset corresponds to a model trained with the ChEMBL or DRD2 sets, wither multi-step (ms) or single-step (ss) and the provenance of the scaffolds (validation set, or non-dataset).</p> <p>The molecules generated are annotated with a set of descriptors. The DRD2 datasets have the predicted probability of each molecule to be active on DRD2 (p) obtained from a Random Forest model. The ChEMBL model's descriptors are related to the synthesizability of the molecules (see manuscript). Also, the datasets decorated from validation set scaffolds are annotated whether they are part of the validation set (in_validation).</p>
Accompanying data - UnCorrupt SMILES: a novel approach to de novo design
<p>This repository contains the files used for "UnCorrupt SMILES: a novel approach to de novo design".</p>
Diverse Hits in de Novo Molecule Design: A Diversity-based Comparison of Goal-directed Generators
<p>Results for the paper "<strong>Diverse Hits in de Novo Molecule Design: A Diversity-based Comparison of Goal-directed Generators"</strong> in the form of the generated molecules and their associated scores. The relevant code to reproduce and visualize the results can be found at https://github.com/ml-jku/diverse-hits. </p> <p> </p>
Dataset for AlphaDesign: A de novo protein design framework based on AlphaFold
<p>This dataset consists of output data from the work reported in: </p> <p>Jendrusch, M., Korbel, J. O., & Sadiq, S. K. (2021). AlphaDesign: A de novo protein design framework based on AlphaFold. bioRxiv.</p> <p> </p> <p> </p>
Data associated with the publication "A de novo designed coiled-coil-based switch regulates the microtubule motor kinesin-1"
<p> All raw data required to reproduce the findings in the manuscript ""A de novo designed coiled-coil-based switch regulates the microtubule motor kinesin-1".</p>
De novo design of a fluorescence-activating β-barrel
<p>This dataset contains the supplementary materials for "<em>De novo</em> design of a fluorescence-activating beta barrel" (doi: 10.1038/s41586-018-0509-0). Detailed description is provided in the "List.docx" file.</p>
Supplementary material 1 from: Kola-Mustapha AT (2023) De novo design of pimarane diterpenoid compounds as potential alternatives to sarecycline for acne vulgaris treatment. Pharmacia 70(4): 1167-1176. https://doi.org/10.3897/pharmacia.70.e113065
Grid box within which Sandaracopimar-15-ene-6.beta., 8.beta.-diol binds is 192.5686 × 260.3472 × 119.6532 along the X, Y, Z-axis
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Validation of de novo designed water-soluble and transmembrane proteins by in silico folding and melting
<p>Here are all of the datasets generated and analysed during this study. </p> <p>Here is a breakdown of their content:</p> <ul> <li><strong>8_stranded_transmembrane_barrels.zip</strong> - raw data from Alphafold (3 and 48 recycles), ESMFold and raptor predictions of the 8 stranded TMBs. A file with all the sequences is also given</li> <li><strong>12_stranded_transmembrane_barrels.zip - </strong>raw data from the Alphafold and ESMfold predictions of the 12 stranded TMBs. A file with all the sequences is also given</li> <li><strong>water_soluble_barrels.zip</strong> - raw data from the Alphafold and ESMfold predictions of the water soluble beta barrels (designable and non-designable). A file with all the sequences is also given</li> <li><strong>all design models.zip</strong> - original design models for water-soluble (designable and non-designable), 8-stranded and 12-stranded TMBs</li> </ul> <p> </p> <ul> <li><strong>ESMfold_masking_exp.tar - </strong>this tar file contains all the ESMfold masking experiments performed to the water-soluble, 8 and 12-stranded transmembrane barrels. Inside there are zipped datasets for each masking experiment<br> </li> <li> <p><strong>ziped_raw_csv_files.zip - </strong>raw csv files with all the data necessary to analyse the figures </p> </li> <li> <p><strong>analysis_notebooks.zip </strong>- Jupyter notebooks used to analyse the output prediction data for all figures</p> </li> </ul> <p> </p> <p> </p>
Data for de novo design of buttressed loops for sculpting protein functions
<p>The design scripts for parametric repeat protein generation and buttressed loop are in buttressed_loops.tar.gz</p> <p>All the design models, protein sequences and DNA sequences are in data.tar.gz</p>
Data associate with the publication "De novo designed peptides for cellular delivery and subcellular localisation"
<p>All raw data required to reproduce the findings in the manuscript "De novo designed peptides for cellular delivery and subcellular localisation"</p>
Dataset for article: "Massively parallel de novo protein design for targeted therapeutics", DOI: 10.1038/nature23912
<p><strong>"Massively parallel de novo protein design for targeted therapeutics" </strong></p> <p>DOI: 10.1038/nature23912 </p> <p><strong>Supplementary Information</strong>. Archive of designs, Rosetta metrics and experimental results.</p> <p>Authors: <strong>Aaron Chevalier*</strong>, <strong>Daniel-Adriano Silva*</strong>, <strong>Gabriel J. Rocklin*</strong>, Derrick R. Hicks, Renan Vergara, Patience Murapa, Steffen M. Bernard, Lu Zhang, Kwok-ho Lam, Guorui Yao, Christopher D. Bahl, Shin-ichiro Miyashita, Inna Goreshnik, James T. Fuller, Merika T. Koday, Cody Jenkins, Tom Colvin, Lauren Carter, Alan Bohn, Cassie M. Bryan, D. Alejandro Fernández-Velasco, Lance Stewart, Min Dong, Xuhui huang, Rongsheng Jin, Ian A. Wilson, Deborah H. Fuller & <strong>David Baker</strong></p> <p><strong>*These authors contributed equally to this work</strong>.</p> <p>Correspondence to: dabaker@uw.edu</p> <p>Dataset Compiled by D-A.S.</p> <p>Date: 13/Sep/2017</p>
Datasets for Target-Specific De Novo Peptide Binder Design with DiffPepBuilder
<p>PepPC-F and PepPC Datasets for Target-Specific De Novo Peptide Binder Design with DiffPepBuilder</p>
Data associated with the publication "Assembling membraneless organelles from de novo designed proteins"
<p>Raw data used in the manuscript "Assembling membraneless organelles from de novo designed proteins"</p>
Multi-domain Distribution Learning for De Novo Drug Design
<p>Model checkpoints, processed dataset and samples.</p>
A generic framework for hierarchical de novo protein design
<p><strong>A small MASTER database</strong> that (most of the time) will be enough for most of the design tasks. The data includes the PDB files <em>master_pdb</em>, PDS files <em>maps</em>, structure fragments <em>frags</em>, ABEG0 torsions <em>master_abego.fa.gz</em>, and secondary structure <em>master_sse.fa.gz</em>.</p>
The structural landscape of the immunoglobulin fold by large-scale de novo design
<p>Dataset for the high-quality immunoglobulin designs.</p>
Data supporting 'De novo-designed minibinders expand the synthetic biology sensing repertoire'
<p>Data supporting Weinberg, Soliman et al. 2024. Manuscript describes data collection practices and experimental design.</p>
DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Human oligo UMI-STARR-seq]
GEO Series GSE183938. Homo sapiens; synthetic construct. 4 samples. Type: Other.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.