DeepSite: protein-binding site predictor using 3D-convolutional neural networks.
TLDR
This work presents a novel knowledge‐based approach that uses state‐of‐the‐art convolutional neural networks, where the algorithm is learned by examples, and demonstrates superior performance to two other competitive algorithmic strategies.Abstract:
Motivation An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein. Results Here we present a novel knowledge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is learned by examples. In total, 7622 proteins from the scPDB database of binding sites have been evaluated using both a distance and a volumetric overlap approach. Our machine-learning based method demonstrates superior performance to two other competitive algorithmic strategies. Availability and implementation DeepSite is freely available at www.playmolecule.org. Users can submit either a PDB ID or PDB file for pocket detection to our NVIDIA GPU-equipped servers through a WebGL graphical interface. Contact gianni.defabritiis@upf.edu. Supplementary information Supplementary data are available at Bioinformatics online.read more
Citations
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Journal ArticleDOI
KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
TL;DR: This work proposes here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compares this approach to other machine- learning and scoring methods using several diverse data sets.
Journal ArticleDOI
Machine-learning-guided directed evolution for protein engineering.
TL;DR: The steps required to build machine-learning sequence–function models and to use those models to guide engineering are introduced and the underlying principles of this engineering paradigm are illustrated with the help of case studies.
Journal ArticleDOI
Development and evaluation of a deep learning model for protein-ligand binding affinity prediction
Marta M. Stepniewska-Dziubinska,Piotr Zielenkiewicz,Piotr Zielenkiewicz,Pawel Siedlecki,Pawel Siedlecki +4 more
TL;DR: A novel deep neural network is developed estimating the binding affinity of ligand‐receptor complexes by utilizing a 3D convolution to produce a feature map of this representation, treating the atoms of both proteins and ligands in the same manner.
Journal ArticleDOI
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics.
Christoph Wehmeyer,Frank Noé +1 more
TL;DR: It is shown that the time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes-beyond the capabilities of linear dimension reduction techniques.
Journal ArticleDOI
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Christoph Wehmeyer,Frank Noé +1 more
TL;DR: In this paper, a modification of an autoencoder type deep neural network was applied to the task of dimension reduction of molecular dynamics data, which can reliably find low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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