Tailored Approach for CACHE Challenge Campaign
Our CACHE challenge campaign integrates several advanced methodologies to create a comprehensive and dynamic drug discovery process. Here’s how we plan to execute this:
Molecular Dynamics (MD) Simulation
We start with MD simulations to provide a detailed, atomic-level analysis of the target protein's behavior. By simulating the movements of atoms and molecules over time, MD reveals dynamic processes and conformational changes in different sites and sub-pockets of the protein structure. Unlike static methods such as X-ray crystallography, MD simulations illustrate how the protein evolves, interacts, and adapts, offering profound insights into stability, flexibility, and interaction patterns with candidate binders under physiological conditions.
Investigation of Critical Interactions
Next, we focus on identifying crucial binding sites and interaction networks within the biological system. This step involves an in-depth analysis of pivotal interactions, essential for understanding the system's biological function. By mapping these interactions, we can pinpoint strategic sites for ligand binding and potential disruption, guiding the rational design of targeted therapies.
Machine Learning (ML)
Leveraging ML techniques, we predict properties and activities of compounds using various molecular descriptors. We train ML models on extensive datasets to discern complex patterns and correlations, enhancing the efficiency and accuracy of the compound screening process. The ability of ML models to continuously learn and improve from new data provides a dynamic and robust tool for accelerating drug discovery and optimizing lead compounds.
Filtering with Selected Cutoffs
We streamline the selection process by applying carefully chosen cutoffs for key parameters. This strategic filtering narrows down the pool of potential compounds, ensuring that only the most promising candidates progress. Setting precise thresholds for critical criteria prioritizes compounds with the highest likelihood of success while removing compounds with unwanted properties (i.e., PAINS, Lilly, BRENK, etc.)
High-Throughput Screening (Molecular Docking)
We utilize high-throughput molecular docking to rapidly screen extensive libraries of compounds, predicting binding affinities and orientations within the target binding site. This approach allows for the efficient evaluation of large numbers of compounds, accelerating the discovery of promising candidates for further development. High-throughput screening quickly generates detailed insights into the interactions between potential drugs and their targets. Using docking scores and a set of other criteria, we select most promising candidate compounds for further forensic manual inspection (so called “cherry-picking party”)
Scaffold Hopping and Bioisosteric Replacement
To explore a broad spectrum of chemical scaffolds capable of binding to the target site, we employ scaffold hopping and bioisosteric replacement. Scaffold hopping explores alternative core structures, while bioisosteric replacement substitutes atoms or functional groups to maintain or enhance biological activity. These techniques enable the discovery of new leads with similar pharmacological profiles but distinct molecular architectures, fostering innovation and efficacy in drug discovery.
Hit-to-Lead Optimization
Upon identifying initial hits, we refine compound properties such as potency, selectivity, and pharmacokinetic profiles through hit-to-lead optimization. This process involves iterative cycles of synthesis and testing, designed to enhance desired characteristics. By systematically modifying the molecular architecture and conducting structure-activity relationship studies, we aim to achieve optimal therapeutic efficacy and safety, transforming promising hits into lead compounds worth of subjecting to preclinical and clinical evaluation.
Next Stage of Molecular Docking in Conjunction and MD Simulation
Our optimized leads would undergo rigorous validation through advanced molecular docking followed by MD simulations. This stage corroborates the binding interactions and structural stability of the leads, ensuring their robustness and desired biological activity. Computational scrutiny provides insights into binding affinities, conformational dynamics, and behavior within the biological milieu, validating the leads' potential and informing further optimization strategies.
Handling Massive Chemical Spaces
Our expertise in handling large chemical spaces, including the REAL Database and REAL Space with over 39 billion molecules, positions us at the forefront of molecular exploration. Using cutting-edge computational methodologies and scalable infrastructure, we efficiently screen and analyze ultra-large compound libraries, accelerating drug discovery and innovation.
Rigorous Parameter Selection and Validation
We meticulously evaluate and validate every parameter at each stage of our workflow against pertinent data. Our approach prioritizes robustness and reliability, ensuring each step is optimized for accurate and meaningful results. While adhering to established virtual screening protocols, we remain flexible and innovative, exploring novel strategies to push the boundaries of drug discovery.
