Energizing Bio-Simulations with Generative AI
When Molecular modelling meets AI, it is done smarter, faster & accurate.
at your finger tips
AI-Integrated Molecular Modelling
We leverage the power of generative artificial intelligence in combination with molecular modeling tools to craft intricate molecular models tailored to diverse biological systems under their natural conditions. Whether your focus is on membrane proteins, cytosolic proteins, protein-DNA/RNA complexes, or small molecule interactions, we excel at modeling these scenarios based on your unique experimental parameters. Our suite of molecular modeling services encompasses the creation of multiscale molecular dynamics simulations, spanning from all-atom precision to coarse-grained and even continuum simulations, all customized to address your specific research challenges. Additionally, we employ various conformational sampling techniques, including umbrella sampling and well-tempered metadynamics. Our overarching objective is to construct a comprehensive model capable of delivering insights into your research inquiries within the relevant time frame of your specific biological processes.
Artificial Intelligence
We harness cutting-edge generative AI and machine learning innovations to streamline the management of vast datasets with heightened automation. Our generative AI models excel at expediting the identification of potential hit and lead compounds while swiftly validating drug targets and optimizing drug structure designs. Throughout every facet of our drug discovery process, our team seamlessly integrates machine learning. This includes predicting the 3D structures of target proteins, forecasting drug-protein interactions, estimating drug activity, crafting novel drug structures via multitarget optimization, repurposing existing drugs for new therapeutic purposes, and forecasting drug toxicity and bioactivity.
Bioinformatics
We harness the capabilities of generative AI to create cutting-edge algorithms and tools dedicated to unlocking valuable insights from vast biological datasets. Our Bioinformatics services encompass the comprehensive examination of Genomics, Proteomics, 3D protein structure modeling, Image analysis, and Drug design. Generative AI and machine learning serve as our primary tools, enabling us to delve deep into molecular-level biological processes and draw meaningful conclusions from the data we gather. Our adept sequence analysis tools boast remarkable speed and precision, facilitating the rapid analysis of DNA and protein sequences to uncover functional clues, identify homologs, perform multiple sequence alignments, search for sequence patterns, and conduct evolutionary analyses. Furthermore, our proficient bioinformatics team specializes in leveraging generative AI to predict protein structures, both at the secondary and tertiary levels, and to dissect the functional aspects of these protein structures.
AI-Driven Protein Engineering
We specialize in protein design automation and protein engineering, with a distinctive focus on harnessing the capabilities of generative AI. Our expertise encompasses the creation of novel ligand-binding sites within proteins and the de novo design of enzymes, resulting in functional enzymes that can undergo optimization to achieve remarkable catalytic efficiencies through directed evolution. We are at the forefront of leveraging conformational dynamics in computational enzyme design, integrating evolutionary insights into the design process, and employing machine learning techniques for protein engineering. Through a synergistic combination of structure-based modeling and cutting-edge deep learning tools, we can identify optimal sequences for your enzyme's desired properties and provide predictions of protein stability following mutation.
AI-Powerd Hit Discovery
Driven by cutting-edge AI capabilities, our distinctive in-house hybrid screening protocol leverages the synergy of deep docking and molecular dynamics simulations to pinpoint potential small molecule hits tailored to your precise targets. This extraordinarily swift virtual screening method has the capacity to assess billions of compounds against diverse conformations of the target proteins, delivering potential hits within a remarkably short timeframe, often in just a matter of days. Our generative AI-powered fragment-based strategy excels at generating drug-like molecules, serving as a highly promising foundation for launching your drug discovery endeavors.
Target Identification & Blind Docking
Our team excels in harnessing the power of generative AI to uncover both potential on-target and off-target interactions for your enigmatic molecules. We employ our highly efficient in-house reverse screening techniques, conducting comprehensive searches across an extensive receptor database that encompasses a wide array of protein families. Our innovative approach seamlessly integrates shape screening, pharmacophore screening, reverse docking, and molecular dynamics simulations. These techniques enable us to decipher the binding mechanisms and modes of action of your drug candidates. Our track record includes remarkable achievements such as identifying new therapeutic applications for existing drugs through drug repositioning, as well as the early detection of adverse drug reactions and drug-related toxicity concerns.
Protein-Protein Interaction Prediction
We harness the power of generative AI alongside protein sequence and structural data to confidently predict protein-protein interactions. Our proprietary algorithms, developed in-house, excel in identifying protein hotspots and foreseeing the interaction interfaces, including allosteric sites within all involved proteins. Our distinctive analysis pipeline evaluates these anticipated interfaces using multiple scoring metrics, offering a comprehensive assessment of their compatibility in terms of shape and physiochemical properties. Subsequently, this data feeds into our molecular modeling pipeline to construct intricate three-dimensional structural models for the envisaged protein-protein interaction complexes. With our protein-protein interaction pipeline, we not only anticipate binding affinities between interacting proteins but also gauge their stability through extensive molecular dynamics simulations.
Binding Affinity Prediction
We combine sophisticated biophysics-based energy-scoring methods with state-of-the-art deep-learning approaches to predict the binding affinities of drugs or antibodies to their target proteins. Our approach ranges from quantum calculations to free energy perturbation to empirical scoring functions with the ultimate goal of obtaining an accurate estimate of the binding for the bound molecules and proteins. Our binding affinity pipeline also uses three-dimensional-convolutional neural networks, spatial graph neural networks, and their fusion to evaluate the binding affinities in an accurate and fast way.
Chemoinformatics and Ligand-Based Drug Design
Our cheminformatics team is expert in analyzing and manipulating big chemical data, including information on small molecule formulas, structures, properties, spectra, and activities (biological or industrial), chemical library design and enumerating virtual libraries. Our team is also expert in ligand-based drug discovery campaigns, including similarity search for bioactive molecules, pharmacophore-based modelling and screening, bioisosteric replacement, and generative AI-based construction of bioactive molecules.