Services provided by Discovery Boulevard
Our vision is to empower researchers worldwide by providing access to cutting-edge bioinformatics tools, training, and consultancy. We aim to bridge the gap between scientific research and real-world innovation, driving impactful discoveries that benefit society
Molecular Docking Studies
We offer complete support for molecular docking and virtual screening, including preparing receptor and ligand libraries, running docking simulations, and analyzing binding interactions. Our services include automation of the docking process using custom Python scripts, enabling efficient handling of large virtual libraries and reducing manual intervention. This approach ensures a streamlined workflow, allowing for high-throughput virtual screening and reliable identification of potential hits in drug discovery projects.
Pharmacophore Modeling
We provide comprehensive support for receptor-based pharmacophore modeling, including identifying key interactions at the receptor site, developing pharmacophore models, and validating them for virtual screening. We also offer detailed validation to confirm the reliability of the pharmacophore model, allowing you to confidently use it for screening and identifying promising drug candidates.
Molecular Dynamics simulation
We offer comprehensive support for molecular dynamics (MD) simulations, covering everything from setting up and running simulations to detailed analysis of protein-ligand interactions, stability, and conformational changes. Our expertise ensures simulations are tailored to meet your specific research needs, providing insights into the dynamic behavior of biomolecular systems. Additionally, we offer consultation on optimizing simulation parameters and interpreting results, helping you derive meaningful insights for drug design and biomolecular studies.
Virtual Screening
Machine learning for cheminformatics
We provide automated QSAR (Quantitative Structure-Activity Relationship) model development services using advanced machine learning techniques, fully implemented in Python. Our approach involves gathering molecular datasets with known biological activities from reliable sources like ChEMBL or PubChem, calculating a wide range of molecular descriptors or fingerprints, and applying machine learning algorithms to build predictive models. We validate these models thoroughly to ensure accuracy and use them for virtual screening and predicting activities of new molecules, streamlining drug discovery and lead optimization.
Network Pharmacology
Our network pharmacology services leverage a systems-level approach to understand drug actions and molecular interactions across biological networks. We identify potential therapeutic targets and elucidate the mechanisms of action by integrating data from multiple sources, including protein-protein interactions, gene regulation, signaling pathways, and drug-target relationships. Using computational tools and algorithms, we create interaction networks that reveal how drugs can affect multiple targets, providing insights into polypharmacology and potential off-target effects.