J. Janet

发表

Machine Learning in Chemistry

H. Kulik, J. Janet, Yamilee Morency, Chemistry majors, Fordham Univers, 2020 .

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Chenru Duan, Aditya Nandy, Jon Paul Janet, 2018, Industrial & Engineering Chemistry Research.

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships.

Heather J Kulik, Jon Paul Janet, H. Kulik, 2017, The journal of physical chemistry. A.

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.

Fang Liu, Chenru Duan, Aditya Nandy, 2019, Journal of chemical theory and computation.

Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

Adam H. Steeves, Jon Paul Janet, Heather J. Kulik, 2017 .

A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery

H. Kulik, J. Janet, A. Nandy, 2019 .

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry.

F. Liu, H. Kulik, J. Janet, 2019, Inorganic chemistry.

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Michael G. Taylor, H. Kulik, J. Janet, 2020, The journal of physical chemistry. A.

Accelerating inorganic discovery with meta-calculation filtering via a decision classifier

Fang Liu, Chenru Duan, Aditya Nandy, 2019 .

Density functional theory for modelling large molecular adsorbate–surface interactions: a mini-review and worked example

H. Kulik, J. Janet, E. Ioannidis, 2016 .

Heterogeneous nucleation in CFD simulation of flashing flows in converging–diverging nozzles

Y. Liao, D. Lucas, J. Janet, 2015 .

Enumeration of de novo inorganic complexes for chemical discovery and machine learning

H. Kulik, J. Janet, Stefan Gugler, 2019, Molecular Systems Design & Engineering.

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

H. Kulik, J. Janet, Chenru Duan, 2019, ACS central science.

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships.

H. Kulik, J. Janet, 2017, The journal of physical chemistry. A.

Communication: Recovering the flat-plane condition in electronic structure theory at semi-local DFT cost.

H. Kulik, Akash Bajaj, J. Janet, 2017, The Journal of chemical physics.

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

H. Kulik, J. Janet, A. Nandy, 2018, Industrial & Engineering Chemistry Research.

Understanding the diversity of the metal-organic framework ecosystem

Peter G. Boyd, Yongjin Lee, S. M. Moosavi, 2020, Nature Communications.

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.

H. Kulik, J. Janet, A. Nandy, 2019, Journal of chemical theory and computation.

Multi-fidelity machine learning models for improved high-throughput screening predictions

S. Kiddle, J. Janet, David Buterez, 2022 .

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Michael G. Taylor, H. Kulik, J. Janet, 2020, The journal of physical chemistry. A.

A quantitative uncertainty metric controls error in neural network-driven chemical discovery

H. Kulik, J. Janet, A. Nandy, 2019, Chemical science.

A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery

H. Kulik, J. Janet, A. Nandy, 2019 .

Ligand additivity relationships enable efficient exploration of transition metal chemical space.

Michael G. Taylor, H. Kulik, J. Janet, 2022, The Journal of chemical physics.

Enumeration of de novo inorganic complexes for chemical discovery and machine learning

Jon Paul Janet, Heather J. Kulik, Stefan Gugler, 2019 .

Enumeration of de novo Inorganic Complexes for Chemical Discovery and Machine Learning

H. Kulik, J. Janet, Stefan Gugler, 2019 .

Predicting electronic structure properties of transition metal complexes with neural networks† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc01247k

Heather J Kulik, Jon Paul Janet, H. Kulik, 2017, Chemical science.

Improving de novo molecular design with curriculum learning

O. Engkvist, J. Janet, C. Margreitter, 2021, Nature Machine Intelligence.

DockStream: a docking wrapper to enhance de novo molecular design

Eva Nittinger, Ola Engkvist, Jon Paul Janet, 2021, Journal of Cheminformatics.

Machine Learning in Chemistry

H. Kulik, J. Janet, Yamilee Morency, Chemistry majors, Fordham Univers, 2020, ACS In Focus.

Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery pdf

Chenru Duan, Aditya Nandy, Jon Paul Janet, 2021, The Journal of chemical physics.

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

Rachel B. Getman, H. Kulik, J. Janet, 2019, ACS Catalysis.

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network.

Heather J Kulik, Jon Paul Janet, Lydia Chan, 2018, The journal of physical chemistry letters.

Artificial intelligence in molecular de novo design: Integration with experiment.

Lewis H. Mervin, O. Engkvist, J. Janet, 2023, Current opinion in structural biology.

De novo design with deep generative models based on 3D similarity scoring.

Kathryn A. Giblin, O. Engkvist, J. Janet, 2021, Bioorganic & medicinal chemistry.

Automated relative binding free energy calculations from SMILES to ΔΔG

J. H. Moore, B. L. de Groot, O. Engkvist, 2023, Communications chemistry.

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design.

H. Kulik, J. Janet, Chenru Duan, 2021, Accounts of chemical research.

A Novel Approach in Malignancy Detection of Computer Aided Diagnosis

E. Kannan, J. Janet, S. Meenalosini, 2012 .

Graph Neural Networks with Adaptive Readouts

S. Kiddle, J. Janet, David Buterez, 2022, NeurIPS.

MF-PCBA: Multifidelity High-Throughput Screening Benchmarks for Drug Discovery and Machine Learning

S. Kiddle, J. Janet, David Buterez, 2023, J. Chem. Inf. Model..

Combining structural and coevolution information to unveil allosteric sites

H. Gohlke, Christopher Pfleger, J. Janet, 2023, Chemical science.

Reusability report: Learning the language of synthetic methods used in medicinal chemistry

Jonas Boström, Jon Paul Janet, Anna Tomberg, 2021, Nature Machine Intelligence.

Link-INVENT: Generative Linker Design with Reinforcement Learning

O. Engkvist, J. Janet, K. Papadopoulos, 2023, Digital Discovery.

Conceptual Project on Using the Pumping Scheme to Eliminate Acid Mine Drainage

K. Harding, K. Rumbold, J. Janet, 2015 .

Modelling local and general quantum mechanical properties with attention-based pooling

J. Janet, David Buterez, Steven J. Kiddle, 2023, Communications chemistry.

Sample efficient reinforcement learning with active learning for molecular design

J. Janet, Jeff Guo, Michael Dodds, 2024, Chemical science.

Predicting electronic structure properties of transition metal complexes with neural networks† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc01247k

H. Kulik, J. Janet, 2017, Chemical science.

Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting

J. Janet, David Buterez, Dino Oglic, 2024, Nature communications.