Redefine BioSimulations with Generative AI
When Molecular Modelling Meets AI - it is done smarter, faster & more efficient.
at your finger tips
AI-Integrated Molecular Modelling
We incorporate generative artificial intelligence with advanced molecular modeling tools to construct highly detailed molecular models tailored to diverse biological systems under natural conditions. Whether your research focuses on membrane proteins, cytosolic proteins, protein-DNA/RNA complexes, or small molecule interactions, our expertise lies in accurately modeling these systems based on your specific experimental parameters. Our comprehensive molecular modeling services encompass the development of multiscale molecular dynamics simulations, ranging from all-atom precision to coarse-grained and continuum-level approaches, each customized to address complex research challenges. Additionally, we employ sophisticated conformational sampling techniques, including umbrella sampling and well-tempered metadynamics, to enhance model accuracy and predictive power. Our ultimate goal is to construct a detailed and reliable model that provides meaningful insights into your research questions within the relevant time frame of your biological processes.
Artificial Intelligence
We utilize advanced generative AI and machine learning to enhance the management of complex datasets with increased automation. Our AI models accelerate the identification of potential hit and lead compounds, efficiently validate drug targets, and optimize drug structure designs. Machine learning is seamlessly embedded throughout our drug discovery process, from predicting the 3D structures of target proteins and modeling drug-protein interactions to estimating drug activity. Our expertise extends to designing novel drug structures through multitarget optimization, repurposing existing drugs for new therapeutic applications, and predicting drug toxicity and bioactivity with precision.
Bioinformatics
We apply generative AI to develop sophisticated algorithms and tools that extract valuable insights from vast biological datasets. Our bioinformatics services encompass comprehensive analyses in Genomics, Proteomics, 3D protein structure modeling, Image Analysis, and Drug Design. By integrating generative AI and machine learning, we investigate molecular-level biological processes, enabling precise and data-driven discoveries. Our advanced sequence analysis tools deliver exceptional speed and accuracy, allowing for rapid DNA and protein sequence examination, functional annotation, homolog identification, multiple sequence alignments, pattern recognition, and evolutionary studies. Additionally, our bioinformatics team leverages generative AI to predict protein structures at both secondary and tertiary levels, providing critical insights into their functional dynamics and interactions.
AI-Driven Protein Engineering
We specialize in protein design automation and protein engineering, with a strong emphasis on generative AI. Our expertise extends to creating novel ligand-binding sites within proteins and designing enzymes from scratch, resulting in functional biomolecules optimized for exceptional catalytic efficiency through directed evolution. Advancing computational enzyme design, we incorporate conformational dynamics, evolutionary insights, and machine learning techniques to refine protein engineering strategies. By combining structure-based modeling with advanced deep learning tools, we identify optimal sequences tailored to specific enzyme properties and provide precise predictions of protein stability following mutation.
AI-Powered Hit Discovery
Driven by advanced AI capabilities, our proprietary hybrid screening protocol combines deep docking and molecular dynamics simulations to identify small molecule hits precisely tailored to specific targets. This highly efficient virtual screening approach can evaluate billions of compounds against multiple conformations of target proteins, delivering potential hits within days. Our generative AI-powered fragment-based strategy further enhances drug discovery by generating drug-like molecules, providing a strong foundation for early-stage development.
Target Identification & Blind Docking
Our team excels in generative AI-driven analysis to uncover both on-target and off-target interactions for complex molecules. Using highly efficient in-house reverse screening techniques, we conduct comprehensive searches across a vast receptor database covering diverse protein families. By combining shape screening, pharmacophore screening, reverse docking, and molecular dynamics simulations, we decode binding mechanisms and modes of action for drug candidates. This approach has led to significant breakthroughs, including identifying new therapeutic applications for existing drugs through repositioning and enabling the early detection of adverse drug reactions and potential toxicity concerns.
Protein-Protein Interaction Prediction
We apply generative AI alongside protein sequence and structural data to accurately predict protein-protein interactions. Our proprietary in-house algorithms excel at identifying protein hotspots and mapping interaction interfaces, including allosteric sites across all involved proteins. Through a specialized analysis pipeline, these predicted interfaces undergo rigorous evaluation using multiple scoring metrics, ensuring a comprehensive assessment of their shape and physiochemical compatibility. This data seamlessly integrates into our molecular modeling framework, enabling the construction of detailed three-dimensional structural models of protein-protein interaction complexes. Beyond predicting binding affinities, our pipeline also assesses stability through extensive molecular dynamics simulations, providing deep insights into interaction dynamics.
Binding Affinity Prediction
We combine advanced biophysics-based energy-scoring methods with deep learning techniques to predict the binding affinities of drugs and antibodies to their target proteins. Our approach spans quantum calculations, free energy perturbation, and empirical scoring functions, all designed to achieve precise binding estimations for molecular interactions. Utilizing three-dimensional convolutional neural networks, spatial graph neural networks, and their fusion, our binding affinity pipeline delivers accurate and efficient evaluations, enabling a deeper understanding of molecular binding dynamics.
Cheminformatics and Ligand-Based Drug Design
Our cheminformatics team excels in analyzing and processing large-scale chemical data, encompassing small molecule formulas, structures, properties, spectra, and biological or industrial activities, as well as chemical library design and virtual library enumeration. With extensive expertise in ligand-based drug discovery, we conduct similarity searches for bioactive molecules, pharmacophore-based modeling and screening, bioisosteric replacement, and the generative AI-driven construction of bioactive compounds, enabling precise and efficient drug discovery efforts.