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protein-ligand conformational ensemble prediction and docking, generating diverse solutions

protein language models via sparse autoencoders, enabling mechanistic understanding of PLM representations

protein embeddings for 236,252 human proteins using ESMC models

Experiments with expanded ensembles to explore chemical space.

protein binding signals from ChIP-seq and sequence motif information

protein expression, pathway activity, etc.), based on the union occurence

protein-ligand complex structure prediction with a multi-scale deep

proteins with potential clinical associations. From CNA and RNA-seq data

protein function by integrating structural and evolutionary constraints into protein

We present a statistical simulator, scDesign3, to generate realistic single

This model is a lightweight model pre-trained on SELFIES

protein language models, enabling programmable CRISPR-Cas nucleases for synthetic

> [!NOTE] > This model has been optimized using NVIDIA

> [!NOTE] > This model has been optimized using NVIDIA

Protein coding genes from a list of candidates. Systematically download

protein's amino acid sequence and full atomistic structure, including

Another list focuses on Python stuff related to Chemistry.

protein sequences and classify them based on their modes of duplication

protein abundance data for CITE-seq, spatial CITE-seq, REAP

The TMSig package contains tools to prepare, analyze, and visualize

protein abundance compared to conventional aggregation methods. This algorithm employs

proteins in flow and mass cytometry data while reducing biases

Protein Data Bank integrating dynamic behaviors and physical properties into

protein interaction networks, and scientific knowledge graph learning across PyTorch

PhenoMol is a bioinformatic framework that integrates comprehensive multi-omic

protein language models demonstrating versatile generative and predictive capabilities for protein

protein structures, presented in the paper JAMUN: Bridging Smoothed Molecular

protein backbone generator utilizing hierarchical fold class labels for conditioning

protein sequences and analyze them. Anchor pairs are detected with

protein partners play a key role in fertility and maintaining

Coralysis is an R package featuring a multi-level integration

protein language model family, including PMC-15B supporting sequence- and structure

protein properties and visualizes position of peptide immunogens within protein

protein binding pocket, enabling scaffold hopping and linker design workflows

# Model details ## Model description Nature Language Model (NatureLM) is a

# Model details ## Model description Nature Language Model (NatureLM) is a

protein function prediction from amino acid sequences using the Gene

protein function prediction from amino acid sequences using the Gene

protein function prediction from amino acid sequences using the Gene

protein activities, enabling rapid protein engineering with minimal experimental data

protein function prediction from amino acid sequences using the Gene

protein function prediction from amino acid sequences using the Gene

protein function prediction from amino acid sequences using the Gene

Universal 3D molecular pretraining framework with 209M conformations, scaling to

RNA foundation model trained on millions of RNA sequences for

proteins trained on thousands of GPUs and Google TPUs using

Diffusion-based molecular docking achieving SOTA blind docking performance, treating

protein markers at the single-cell level, producing high dimensional

protein. This is an example for finetuning ibm/biomed.omics.bl.sm-ted-458m for protein

protein, step by step, using the BioExcel Building Blocks (biobb