Molecular Docking

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. Protheragen 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

nomain-drag-pic1Methods

Computer Aided Drug Design Technologies (Physics-based)
Artificial Intelligence (Experiences-based)

nomain-drag-pic1Datasets

  • nomain-title-log-pic2 Directory of Useful Decoys – Enhanced (DUD-E)
    A dataset designed to help benchmark structure-based virtual screening methods including 102 targets, whose decoys were selected from ZINC, 50 decoys for each active having similar physicochemical properties but dissimilar 2-D topology. There are classical targets of different protein families in DUD-E dataset, including kinase, protease, nuclear receptor, GPCR, and others.
  • nomain-title-log-pic2 Maximum Unbiased Validation (MUV)
    A dataset that is equally unbiased for assessment of the quality of virtual screening methods, each target with ~30 actives and 15,000 decoys, whose decoys are selected from a primary screen (PubChem). There are classical targets of different protein families in MUV dataset, including kinase, protease, nuclear receptor, PPI, and others.
  • nomain-title-log-pic2 Dataset of Proteins with Their Possible Ligands (PDB, PDBbind, Binding DB, DUD etc.)

nomain-drag-pic1Docking and Scoring Software Programs

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.

  • nomain-title-log-pic1 AutoDock, eHiTS, iDock, etc.
  • nomain-title-log-pic1 Smina(CADD), Glide SP, AtomNet, etc.

nomain-drag-pic1Model Input

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Atom type

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Atomic partial charges

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Amino acid types

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Distances from neighbors to the reference atom

nomain-drag-pic1Model Structure

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* 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."

nomain-drag-pic1Highlights

  • nomain-title-log-pic2 Structure-based virtual screening is an important tool for compound prioritization. Experiences-based scoring function works well on such field, which can displace traditional physics-based way (CADD).
  • nomain-title-log-pic2 Deep learning-based method developed by Protheragen's experts provides an alternative and promising way to prioritize compound during screening.
  • nomain-title-log-pic2 The universe docking model (trained by mixed data such as DUD-E) works well in cross-validation.
  • nomain-title-log-pic2 Target-specific docking model perform much better than universe model in independent validation.
  • nomain-title-log-pic2 Protheragen provides a new solution to solve the generalization issue of deep learning based docking system for virtual screening.

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