natural-language-processing-11

molecular representation

3260 papers • 126 benchmarks • 313 datasets

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Use these libraries to find molecular-representation-11 models and implementations

PaddlePaddle/PaddleHelix

2 papers 787

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Most implemented papers

Analyzing Learned Molecular Representations for Property Prediction

Connor W. Coley, Kevin Yang, Wengong Jin, T. Jaakkola, R. Barzilay, Kyle Swanson, Philipp Eiden, Hua Gao, A. Guzman-Perez, Timothy Hopper, Brian P. Kelley, M. Mathea, Andrew Palmer, Volker Settels, K. Jensen•Mon Apr 01 2019

A graph convolutional model is introduced that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets.

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Self-Supervised Graph Transformer on Large-Scale Molecular Data

Yatao Bian, Yu Rong, Junzhou Huang, Tingyang Xu, Wei-yang Xie, Ying Wei, Wen-bing Huang•Wed Jun 17 2020

A novel framework, GROVER, which stands for Graph Representation frOm self-supervised mEssage passing tRansformer, which allows it to be trained efficiently on large-scale molecular dataset without requiring any supervision, thus being immunized to the two issues mentioned above.

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ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Bharath Ramsundar, Seyone Chithrananda, Gabriel Grand•Sun Oct 18 2020

This work makes one of the first attempts to systematically evaluate transformers on molecular property prediction tasks via the ChemBERTa model, and suggests that transformers offer a promising avenue of future work for molecular representation learning and property prediction.

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Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations

F. Noé, Djork-Arné Clevert, R. Winter, F. Montanari•Sun Jul 29 2018

Translation between semantically equivalent but syntactically different line notations of molecular structures compresses meaningful information into a continuous molecular descriptor.

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Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

Alán Aspuru-Guzik, Pascal Friederich, Mario Krenn, Florian Hase, AkshatKumar Nigam•Thu May 30 2019

SELFIES (SELF-referencIng Embedded Strings), a string-based representation of molecules which is 100% robust and allows for explanation and interpretation of the internal working of the generative models.

830 0

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Molecular representation learning with language models and domain-relevant auxiliary tasks

Marwin H. S. Segler, Marco Fiscato, Benedek Fabian, T. Edlich, H. Gaspar, Joshua Meyers, Mohamed Ahmed•Wed Nov 25 2020

The Transformer architecture is applied, specifically BERT, to learn flexible and high quality molecular representations for drug discovery problems, and molecular representations learnt by the model `MolBert' improve upon the current state of the art on the benchmark datasets.

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Multiresolution equivariant graph variational autoencoder

R. Kondor, T. Hy•Tue Jun 01 2021

The proposed framework is end-to-end permutation equivariant with respect to node ordering and achieves competitive results with several generative tasks including general graph generation, molecular generation, unsupervised molecular representation learning to predict molecular properties, link prediction on citation graphs, and graph-based image generation.

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Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching

Shengchao Liu, Jian Tang, Hongyu Guo•Sun Jun 26 2022

GeoSSL is proposed, a 3D coordinate denoising pretraining framework to model such an energy landscape based on the dynamic nature of 3D molecules, where the continuous motion of a molecule in the 3D Euclidean space forms a smooth potential energy surface.

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Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs

Rui Jiao, Yu Rong, Wenbing Huang, Jiaqi Han, Yang Liu•Sun Jul 17 2022

This work proposes to adopt an equivariant energy-based model as the backbone for pretraining, which enjoys the merits of fulfilling the symmetry of 3D space, and develops a node-level pretraining loss for force prediction, where the Riemann-Gaussian distribution is exploited to ensure the loss to be E(3)-invariant, enabling more robustness.

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