One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage …
read moreDeepFrag is a machine learning model for fragment-based lead optimization. In this repository, you will find code to train the model and code to run inference using a pre-trained model.
read moreDeepFrag is a machine learning model for fragment-based lead optimization. In this repository, you will find code to train the model and code to run inference using a pre-trained model.
read moreWe propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with …
read moreThere has been increasing interest in the use of deep neural networks for de novo design of molecules with desired properties. A common approach is to train a generative model on SMILES strings and then use this to generate SMILES strings for molecules with a desired property. Unfortunately, these SMILES …
read moreWe report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures …
read moreMagpie is an extensible platform for using machine learning to predict the properties of materials. Magpie is also an acronym for “Material-Agnostic Platform for Informatics and Exploration”, and is named after an intelligent bird.
read moreExciting advances have been made in artificial intelligence (AI) during recent decades. Among them, applications of machine learning (ML) and deep learning techniques brought human-competitive performances in various tasks of fields, including image recognition, speech recognition, and natural language understanding. Even in Go, the ancient game of profound complexity, the …
read moreThe JARVIS-Tools is an open-access software package for atomistic data-driven materials desgin. JARVIS-Tools can be used for a) setting up calculations, b) analysis and informatics, c) plotting, d) database development and e) web-page development. JARVIS-Tools empowers NIST-JARVIS (Joint Automated Repository for Various Integrated Simulations) repository which is an integrated framework …
read moreIRNet is a general purpose deep residual regression framework that contains model architectures composed of fully connected layers of different depths (17-layers, 24-layers and 48-layers) for data mining problems with numerical vectors as inputs. Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments …
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