Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins

How can AI accelerate advances in physiology?

(2023) Andrew D. McCulloch   JOURNAL OF GENERAL PHYSIOLOGY

Single-shot forward and inverse hierarchical architected materials design for nonlinear mechanical properties using an Attention-Diffusion model

(2023) Andrew J. Lew et al.   Materials Today

A computational building block approach towards multiscale architected materials analysis and design with application to hierarchical metal metamaterials

(2023) Markus J Buehler   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model

(2023) Bo Ni et al.   Chem

End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins

(2022) Chi-Hua Yu et al.   ACS Biomaterials-Science & Engineering

Generative design, manufacturing, and molecular modeling of 3D architected materials based on natural language input

(2022) Yu-Chuan Hsu et al.   APL Materials

FieldPerceiver: Domain agnostic transformer model to predict multiscale physical fields and nonlinear material properties through neural ologs

(2022) Markus J. Buehler   Materials Today

On the dissipation of conforming and discontinuous Galerkin schemes for the incompressible Navier–Stokes equations

(2022) Xi Chen et al.   AIP Advances

Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model

(2022) Markus J. Buehler   JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME

End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification

(2022) Yiwen Hu et al.   ACS Nano

Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map

(2021) Jianwen Chen et al.   Journal of Cheminformatics

Machine Learning‐Driven Biomaterials Evolution

(2021) Ady Suwardi et al.   ADVANCED MATERIALS

DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks

(2021) Mohammad Madani et al.   INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

The trRosetta server for fast and accurate protein structure prediction

(2021) Zongyang Du et al.   Nature Protocols

Words to Matter: De novo Architected Materials Design Using Transformer Neural Networks

(2021) Zhenze Yang et al.   Frontiers in Materials

Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction

(2019) Philippe Schwaller et al.   ACS Central Science

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

(2018) Daniel Merk et al.   Molecular Informatics

Biopolymer nanofibrils: Structure, modeling, preparation, and applications

(2018) Shengjie Ling et al.   PROGRESS IN POLYMER SCIENCE

Nanofibrils in nature and materials engineering

(2018) Shengjie Ling et al.   Nature Reviews Materials

Deep reinforcement learning for de novo drug design

(2018) Mariya Popova et al.   Science Advances

Multiscale Modeling of Silk and Silk-Based Biomaterials-A Review

(2018) Diego López Barreiro et al.   MACROMOLECULAR BIOSCIENCE

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

(2017) Sheng Wang et al.   PLoS Computational Biology

Hierarchical Coexistence of Universality and Diversity Controls Robustness and Multi-Functionality in Protein Materials

(2016) Theodor Ackbarow et al.   Journal of Computational and Theoretical Nanoscience

Category Theory Based Solution for the Building Block Replacement Problem in Materials Design

(2012) Tristan Giesa et al.   ADVANCED ENGINEERING MATERIALS

A review of combined experimental and computational procedures for assessing biopolymer structure–process–property relationships

(2012) Greta Gronau et al.   BIOMATERIALS

Category Theoretic Analysis of Hierarchical Protein Materials and Social Networks

(2011) David I. Spivak et al.   PLoS One

A series of PDB related databases for everyday needs

(2010) R. P. Joosten et al.   NUCLEIC ACIDS RESEARCH