Machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. A computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. Molecular docking of small molecules in the protein binding sites is the most widely used computational technique in modern structure-based drug discovery. MedAI has the state-of-the-art machine learning (ML) techniques in computational docking. Computational docking is the process of predicting the best pose (orientation + conformation) of a small molecule (drug candidate) when bound to a target larger receptor molecule (protein) in order to form a stable complex molecule.
Step 1. Docking
Step 2. Scoring
Computer Aided Drug Design Technologies (Physics-based)
Artificial Intelligence (Experiences-based)
Deep learning systems, as convolutional neural networks (CNN) implementations have been previously used to create a function that predicts the free energy of molecular binding (a score) using the structural information generated by docking software. Our molecular dynamics (MD)-based protocols are capable in estimating the free energy of binding between the ligand and target protein.
Atomic partial charges
Amino acid types
Distances from neighbors to the reference atom
* A sigmoid function is a type of activation function, and more specifically defined as a squashing function. Squashing functions limit the output to a range between 0 and 1, making these functions useful in the prediction of probabilities. Sigmoidal functions are frequently used in machine learning, specifically in the testing of artificial neural networks, as a way of understanding the output of a node or "neuron."
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