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PP-MAPS is an open-source workflow designed to extract and aggregate dynamic pharmacophore signatures from molecular dynamics (MD) trajectories of protein-peptide complexes. It helps characterize protein-peptide interactions critical for drug discovery by producing per-residue interaction frequencies and pharmacophore heatmaps.

An automated framework that maps small organic molecules from SMILES strings into standardized Martini 3 coarse-grained force field models for molecular dynamics simulations.

NaviDiv is a web application designed to monitor and manage chemical diversity in the generative design of molecules. It provides multi-metric analysis, real-time visualization, and adaptive constraint generation to guide molecular optimization while preserving chemical diversity.

TransBindpMHCI is a transformer-based model designed for predicting MHC-I peptide binding, which is crucial for identifying immunogenic neoantigens in cancer immunotherapy.

pyKinaXe is a Python package providing automated, end-to-end analysis for peptide microarray data (PamChip®). It profiles kinase activity by extracting kinase information and quantifying phosphorylation kinetics from raw microarray images.

TorsiFlex is an automatic generator of torsional conformers. Application to the twenty proteinogenic amino acids. TorsiFlex is an user-friendly program written in Python 3. It was designed to seek the conformers of a given molecule by using a combined low-level/high-level (LL/HL) methodology.

Deep Fingerprint Learn (DFPL) is an aI for predicting chemical-effect associations at the universe level. Link molecular structures of chemicals (in form of topological fingerprints) with multiple targets.

BioMetAll is a command line application to allow the identification of metal-binding sites in proteins from backbone preorganization.

DockQ is a software tool for assessing the quality of modeled biomolecules, including proteins, nucleic acids, and small molecules. The tool has been reimplemented in pure Python, featuring improved portability, speed, and novel functionalities like automatic chain mapping and calculations for multiple interfaces.

Web server for predicting SAM binding residues of a protein from its amino acid sequence.

AutoGraph performs autonomous graph based clustering on metabolite conformers to reduce redundancy from configuration space. AutoGraph prioritizes autonomation, simplicity, and practicality.

Pathogenicity prediction of human missense variants based on protein sequence, structure and dynamics | computationally predicting effects of saturation mutagenesis | In silico saturation mutagenesis | perform a complete scan of all possible 19 amino acid substitutions at each site on a human sequence. See example: H-Ras

DTI-Voodoo is a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein protein interaction networks, and uses a graph neural networks to predict drug-target interactions.

DeepDrug is a general graph-based deep learning framework for drug-drug/protein interaction prediction.

Analysis pipeline for the consistent, reproducible and high-resolution structural analysis of bacterial peptidoglycans.

A Python script to predict peptide MHC binding and presentation with BERT model. Improves MHC-peptide class II interaction prediction with transformer and multiple instance learning.

A Deep Learning Framework for Drug Synergy Prediction. Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects.

A Genome-Mining Tool for Class-Independent RiPP Discovery. Bioinformatic application for the detection of RREs in protein sequences of interest. Install the included conda environment with "conda env create -f RREfinder.yml" Or make sure the following packages/programs are installed and in your path. Databases need to be downloaded independently from https://zenodo.org/record/3733240#.XoRkIUEzbCI. Please download all the files, except the folder with the seed sequences, and place them into the data/database folder within the downloaded repository. RREFinder accepts both protein .fasta files and .genbank files. Alternatively, you can use '--antismash ripp' and let the --infile point to a .final.gbk file from antiSMASH analysis. This way all the antiSMASH RiPP gene clusters will be analyzed.