Nature - When natural proteins folds to produce their unique biologically adapted structure, they do so despite the many energetically unfavorable non-ideal features — such as kinked helices,...

List of papers about Proteins Design using Deep Learning - Peldom/papers_for_protein_design_using_DL

Nature Biotechnology - De novo protein design with a language model

Nature - Modelling by SCUBA of the backbone-centred energy surface extends the diversity of designable proteins.

Nature Communications - Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Here the...

Nature Biotechnology - What does it take for an ‘AlphaFold Moment’ in functional protein engineering and design?

Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the primary motivation for de novo protein design was to test our understanding of the informational aspect of the...

Nature - A reinforcement learning approach based on AlphaZero is used to discover efficient and provably correct algorithms for matrix multiplication, finding faster algorithms for a variety of...

The design of complex self-organising systems producing life-like phenomena, such as the open-ended evolution of virtual creatures, is one of the main goals of artificial life. Lenia, a family of...

Nature - Advances in artificial intelligence are at the heart of many of this year’s most exciting areas of technological innovation

Nature Computational Science - A deep learning method for protein sequence design on given backbones, ABACUS-R, is proposed in this study. ABACUS-R shows an improved performance when compared with...

Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that...

We are seeking a highly motivated and innovative individual for a research position in the field of biomedical engineering, biotechnology, bioengineering or a related discipline. Interest in techno...

Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language models. We propose...

Nature Communications - Advances in protein structure prediction have led to a significant influx of protein structure data. Here the authors exploit this data to offer an unbiased overview of...

Hablar de diseño de proteínas por Inteligencia Artificial (IA) puede sonar a ciencia ficción, pero desde hace unos años es sólo ciencia. A secas. Y

Abstract. The experimental characterization and computational prediction of protein structures has become increasingly rapid and precise. However, the anal

Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models. Deep-HP increases DNNs' MD capabi...

Nature Machine Intelligence - One of the main advances in deep learning in the past five years has been graph representation learning, which enabled applications to problems with underlying...

The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing...

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid...

:sparkles::sparkles:Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation. - BradyFU/Awesome-Multimodal-Large-Language-Models

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