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. CD ComputaBio 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.
Atom type
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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