| scholarly article | Q13442814 |
| P818 | arXiv ID | 1701.04503 |
| P8978 | DBLP publication ID | journals/jcc/GohHV17 |
| P356 | DOI | 10.1002/JCC.24764 |
| P698 | PubMed publication ID | 28272810 |
| P50 | author | Nathan Hodas | Q104530806 |
| Garrett B Goh | Q88918615 | ||
| P2093 | author name string | Abhinav Vishnu | |
| P2860 | cites work | General atomic and molecular electronic structure system | Q56017006 |
| Deep Blue | Q56083776 | ||
| Principles of QSAR models validation: internal and external | Q56432346 | ||
| NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations | Q56485185 | ||
| ImageNet Large Scale Visual Recognition Challenge | Q56594393 | ||
| Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties | Q56919389 | ||
| Machine learning of molecular electronic properties in chemical compound space | Q56919402 | ||
| Consensus Modeling for HTS Assays Using In silico Descriptors Calculates the Best Balanced Accuracy in Tox21 Challenge | Q57898145 | ||
| Identifying Biological Pathway Interrupting Toxins Using Multi-Tree Ensembles | Q57898228 | ||
| Commentary: The Materials Project: A materials genome approach to accelerating materials innovation | Q58480805 | ||
| Progress and challenges in protein structure prediction | Q22251179 | ||
| Gapped BLAST and PSI-BLAST: a new generation of protein database search programs | Q24545170 | ||
| Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction | Q24655241 | ||
| Consistent blind protein structure generation from NMR chemical shift data | Q24657929 | ||
| CHARMM: the biomolecular simulation program | Q24658108 | ||
| PISCES: recent improvements to a PDB sequence culling server | Q24812503 | ||
| High-throughput sequencing for biology and medicine | Q26823498 | ||
| PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints | Q27065420 | ||
| Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning | Q27144201 | ||
| Scalable molecular dynamics with NAMD | Q27860718 | ||
| The Amber biomolecular simulation programs | Q27860745 | ||
| GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation | Q27860944 | ||
| Accurate and efficient target prediction using a potency-sensitive influence-relevance voter | Q27902298 | ||
| Mastering the game of Go with deep neural networks and tree search | Q28005460 | ||
| Deep learning | Q28018765 | ||
| Quantitative nanostructure-activity relationship modeling | Q28385022 | ||
| Framewise phoneme classification with bidirectional LSTM and other neural network architectures. | Q51966055 | ||
| Progress in predicting inter-residue contacts of proteins with neural networks and correlated mutations. | Q52046751 | ||
| Robust QSAR models using Bayesian regularized neural networks. | Q52207019 | ||
| DeepTox: Toxicity Prediction using Deep Learning | Q55920548 | ||
| Best Practices for QSAR Model Development, Validation, and Exploitation | Q28649930 | ||
| Protein structure prediction from sequence variation | Q28914772 | ||
| The art and practice of structure-based drug design: A molecular modeling perspective | Q29029768 | ||
| Evaluation of residue-residue contact predictions in CASP9 | Q29048165 | ||
| Learning representations by back-propagating errors | Q29469983 | ||
| Statistical potential for assessment and prediction of protein structures | Q29615145 | ||
| Extended-connectivity fingerprints | Q29616639 | ||
| Review: protein secondary structure prediction continues to rise. | Q30328632 | ||
| The ASTRAL Compendium in 2004 | Q30336507 | ||
| Energy landscapes and solved protein-folding problems. | Q30350024 | ||
| PROFcon: novel prediction of long-range contacts. | Q30350709 | ||
| A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. | Q30350835 | ||
| CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data | Q30351917 | ||
| Improved residue contact prediction using support vector machines and a large feature set. | Q30360864 | ||
| Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network. | Q30366705 | ||
| Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts | Q30375594 | ||
| Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. | Q30375959 | ||
| A deep learning framework for modeling structural features of RNA-binding protein targets | Q30390089 | ||
| Predicting residue-residue contacts using random forest models | Q30408474 | ||
| Deep architectures for protein contact map prediction | Q30419830 | ||
| Theory of protein folding: the energy landscape perspective | Q30428743 | ||
| Exploring chemical space for drug discovery using the chemical universe database | Q30485721 | ||
| Big-deep-smart data in imaging for guiding materials design. | Q30995920 | ||
| Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach | Q31024412 | ||
| Development and large scale benchmark testing of the PROSPECTOR_3 threading algorithm | Q31086785 | ||
| Stalking the Materials Genome: A Data-Driven Approach to the Virtual Design of Nanostructured Polymers. | Q31121706 | ||
| Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? | Q32032942 | ||
| Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics | Q33344983 | ||
| Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data | Q33444062 | ||
| Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway | Q33883400 | ||
| A fast learning algorithm for deep belief nets | Q33996665 | ||
| PubChem's BioAssay Database | Q34090643 | ||
| How fast-folding proteins fold | Q34228223 | ||
| Are Protein Force Fields Getting Better? A Systematic Benchmark on 524 Diverse NMR Measurements | Q34285601 | ||
| Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules | Q34353087 | ||
| Deep learning in neural networks: an overview | Q34451415 | ||
| Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning | Q34487019 | ||
| Machine-learning-assisted materials discovery using failed experiments | Q34525044 | ||
| Advances in methods and algorithms in a modern quantum chemistry program package. | Q34556469 | ||
| Physically realistic homology models built with ROSETTA can be more accurate than their templates | Q34600150 | ||
| Accelerating molecular modeling applications with graphics processors | Q34692912 | ||
| XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks. | Q35041769 | ||
| A Kirkwood-Buff Derived Force Field for Aqueous Alkali Halides | Q35121066 | ||
| SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles | Q35618898 | ||
| Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations | Q35777486 | ||
| Quantitative Structure-Fluorescence Property Relationship Analysis of a Large BODIPY Library | Q36087361 | ||
| Predicting protein residue-residue contacts using deep networks and boosting | Q36432211 | ||
| Evaluation of methods for modeling transcription factor sequence specificity | Q36941693 | ||
| Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise | Q37053828 | ||
| Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. | Q37209287 | ||
| Influence relevance voting: an accurate and interpretable virtual high throughput screening method | Q37360048 | ||
| 3D-QSAR in drug design--a review | Q37636264 | ||
| Quantitative structure-property relationship modeling of diverse materials properties. | Q37976495 | ||
| Representation learning: a review and new perspectives | Q38115908 | ||
| Applications of Deep Learning in Biomedicine | Q38786217 | ||
| Deep Learning in Drug Discovery. | Q38918718 | ||
| A simple model predicts UGT-mediated metabolism. | Q39669361 | ||
| Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. | Q40028855 | ||
| Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies | Q40302910 | ||
| Artificial evolution of coumarin dyes for dye sensitized solar cells. | Q40476663 | ||
| Accurate modeling of ionic surfactants at high concentration | Q41012679 | ||
| Improved Prediction of CYP-Mediated Metabolism with Chemical Fingerprints. | Q41076278 | ||
| Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione. | Q41289680 | ||
| Deep neural nets as a method for quantitative structure-activity relationships | Q41506565 | ||
| Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking | Q42251037 | ||
| Contact prediction using mutual information and neural nets | Q44547214 | ||
| Searching for exotic particles in high-energy physics with deep learning. | Q45956792 | ||
| Prediction of fungicidal activities of rice blast disease based on least-squares support vector machines and project pursuit regression. | Q45964325 | ||
| Classifying 'drug-likeness' with kernel-based learning methods. | Q45966475 | ||
| Deep Learning for Drug-Induced Liver Injury | Q46659607 | ||
| Predicting physical properties of ionic liquids | Q46947635 | ||
| Random forest: a classification and regression tool for compound classification and QSAR modeling | Q47370701 | ||
| Accelerating Density Functional Calculations with Graphics Processing Unit. | Q51872067 | ||
| P433 | issue | 16 | |
| P921 | main subject | quantum chemistry | Q188403 |
| deep learning | Q197536 | ||
| machine learning | Q2539 | ||
| Materials Genome | Q6786587 | ||
| computational chemistry | Q369472 | ||
| protein structure prediction | Q899656 | ||
| toxicology | Q7218 | ||
| P304 | page(s) | 1291-1307 | |
| P577 | publication date | 2017-03-08 | |
| P1433 | published in | Journal of Computational Chemistry | Q3186908 |
| P1476 | title | Deep learning for computational chemistry | |
| P478 | volume | 38 |
| Q113241744 | AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis |
| Q98178435 | Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning |
| Q57048561 | Artificial Intelligence in Drug Design |
| Q108127160 | Artificial intelligence to deep learning: machine intelligence approach for drug discovery |
| Q47780833 | Automating drug discovery |
| Q108768061 | Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems |
| Q52573365 | Computational Protein Design with Deep Learning Neural Networks. |
| Q89208348 | Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation |
| Q50516673 | Computer-aided drug design: time to play with novel chemical matter. |
| Q89623133 | Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns |
| Q62496471 | Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era |
| Q89759224 | Deep Learning-driven research for drug discovery: Tackling Malaria |
| Q64235837 | Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses |
| Q91511799 | Deep learning in bioinformatics: Introduction, application, and perspective in the big data era |
| Q52309127 | Deep learning in pharmacogenomics: from gene regulation to patient stratification. |
| Q58195573 | DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning |
| Q90652105 | Designing Algorithms To Aid Discovery by Chemical Robots |
| Q58763500 | Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method |
| Q89900229 | Dinucleoside polyphosphates act as 5'-RNA caps in bacteria |
| Q38617915 | Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models. |
| Q64972663 | Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework. |
| Q94204611 | Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation |
| Q59791381 | GPU Accelerated Quantum Virtual Screening: Application for the Natural Inhibitors of New Dehli Metalloprotein (NDM-1) |
| Q50015629 | Generative Models for Artificially-intelligent Molecular Design. |
| Q92260021 | Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network |
| Q50420418 | In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts. |
| Q64971074 | Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations. |
| Q90093766 | Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests |
| Q49384986 | Learning a Local-Variable Model of Aromatic and Conjugated Systems. |
| Q112559713 | Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification |
| Q95302062 | Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations |
| Q92669180 | Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry |
| Q64922267 | Machine learning material properties from the periodic table using convolutional neural networks. |
| Q94060053 | Open-source QSAR models for pKa prediction using multiple machine learning approaches |
| Q52331906 | Opportunities and obstacles for deep learning in biology and medicine. |
| Q114061420 | Predicting protein network topology clusters from chemical structure using deep learning |
| Q60937458 | Predicting tumor cell line response to drug pairs with deep learning |
| Q91977367 | Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning |
| Q59808654 | QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery |
| Q55963652 | Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases |
| Q100945973 | Synthesis of computer simulation and machine learning for achieving the best material properties of filled rubber |
| Q90447656 | The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation |
| Q90518896 | Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts |
| Q102136115 | kGCN: a graph-based deep learning framework for chemical structures |
Search more.