Mingxuan Li

Mingxuan Li

MLE | AI4Science | Drug Discovery

Posts by Tags

Cheminformatics

Literature Notes - MolFormer

1 minute read

Published:

The study highlights the capabilities of the Molecule Transformer (M-Transformer), an unsupervised, pretrained molecular language model that excels in predicting molecular properties from SMILES sequences. This model surpasses traditional graph-based models in various benchmarks, efficiently utilizes computational resources by reducing GPU usage by a factor of 60, and accurately captures interatomic relationships. Further exploration into expanding its applicability beyond small organic molecules is recommended.

Foundation Model

Literature Notes - Geneformer

2 minute read

Published:

  • Traditional methods for analyzing single-cell transcriptomics rely on cell-type-specific annotations or perturbation-based studies, which can be resource-intensive and dataset-specific.
  • Geneformer, a transformer-based foundation model, learns transcriptional dynamics directly from large-scale unlabelled single-cell transcriptomes, enabling context-aware gene mapping across diverse cellular states and conditions.
  • Pretrained on Genecorpus-30M (29.9 million single-cell transcriptomes), Geneformer provides a robust framework for a wide range of downstream applications, from dosage sensitivity prediction to in silico gene perturbation analysis.

Literature Notes - ESM3

1 minute read

Published:

  • ESM3 pushes the boundaries of protein design by integrating sequence, structure, and function into a single unified framework.
  • It scales up to 98 billion parameters and leverages multimodal inputs, enabling generative design and evolutionary insights.
  • Simulates evolutionary processes, producing proteins equivalent to 500 million years of natural evolution within days.

Literature Notes - ESM2

less than 1 minute read

Published:

  • Traditional protein prediction models rely heavily on evolutionary information, often requiring computationally expensive multiple sequence alignment (MSA) inputs.
  • ESM2, powered by large language models (LLMs), learns evolutionary patterns directly from raw protein sequences, eliminating MSA requirements and simplifying the computational pipeline.
  • Achieves a ~60x faster inference compared to prior state-of-the-art methods, facilitating studies on vast metagenomic datasets (e.g., MGnify90).

Literature Notes - Physics-informed machine learning

1 minute read

Published:

The text discusses the advancements in Physics-Informed Neural Networks (PINNs) facilitated by their integration with modern machine learning libraries such as TensorFlow, PyTorch, and specialized frameworks like DeepXDE and SimNet. PINNs excel in incorporating physical laws into machine learning models, making them particularly adept at handling complex, multidimensional scientific problems.

Literature Notes - MolFormer

1 minute read

Published:

The study highlights the capabilities of the Molecule Transformer (M-Transformer), an unsupervised, pretrained molecular language model that excels in predicting molecular properties from SMILES sequences. This model surpasses traditional graph-based models in various benchmarks, efficiently utilizes computational resources by reducing GPU usage by a factor of 60, and accurately captures interatomic relationships. Further exploration into expanding its applicability beyond small organic molecules is recommended.

Gene Networks

Literature Notes - Geneformer

2 minute read

Published:

  • Traditional methods for analyzing single-cell transcriptomics rely on cell-type-specific annotations or perturbation-based studies, which can be resource-intensive and dataset-specific.
  • Geneformer, a transformer-based foundation model, learns transcriptional dynamics directly from large-scale unlabelled single-cell transcriptomes, enabling context-aware gene mapping across diverse cellular states and conditions.
  • Pretrained on Genecorpus-30M (29.9 million single-cell transcriptomes), Geneformer provides a robust framework for a wide range of downstream applications, from dosage sensitivity prediction to in silico gene perturbation analysis.

LLM

Literature Notes - ESM3

1 minute read

Published:

  • ESM3 pushes the boundaries of protein design by integrating sequence, structure, and function into a single unified framework.
  • It scales up to 98 billion parameters and leverages multimodal inputs, enabling generative design and evolutionary insights.
  • Simulates evolutionary processes, producing proteins equivalent to 500 million years of natural evolution within days.

Literature Notes - ESM2

less than 1 minute read

Published:

  • Traditional protein prediction models rely heavily on evolutionary information, often requiring computationally expensive multiple sequence alignment (MSA) inputs.
  • ESM2, powered by large language models (LLMs), learns evolutionary patterns directly from raw protein sequences, eliminating MSA requirements and simplifying the computational pipeline.
  • Achieves a ~60x faster inference compared to prior state-of-the-art methods, facilitating studies on vast metagenomic datasets (e.g., MGnify90).

Literature Notes

Literature Notes - Physics-informed machine learning

1 minute read

Published:

The text discusses the advancements in Physics-Informed Neural Networks (PINNs) facilitated by their integration with modern machine learning libraries such as TensorFlow, PyTorch, and specialized frameworks like DeepXDE and SimNet. PINNs excel in incorporating physical laws into machine learning models, making them particularly adept at handling complex, multidimensional scientific problems.

Literature Notes - MolFormer

1 minute read

Published:

The study highlights the capabilities of the Molecule Transformer (M-Transformer), an unsupervised, pretrained molecular language model that excels in predicting molecular properties from SMILES sequences. This model surpasses traditional graph-based models in various benchmarks, efficiently utilizes computational resources by reducing GPU usage by a factor of 60, and accurately captures interatomic relationships. Further exploration into expanding its applicability beyond small organic molecules is recommended.

PINNs

Literature Notes - Physics-informed machine learning

1 minute read

Published:

The text discusses the advancements in Physics-Informed Neural Networks (PINNs) facilitated by their integration with modern machine learning libraries such as TensorFlow, PyTorch, and specialized frameworks like DeepXDE and SimNet. PINNs excel in incorporating physical laws into machine learning models, making them particularly adept at handling complex, multidimensional scientific problems.

Protein Evolution

Literature Notes - ESM3

1 minute read

Published:

  • ESM3 pushes the boundaries of protein design by integrating sequence, structure, and function into a single unified framework.
  • It scales up to 98 billion parameters and leverages multimodal inputs, enabling generative design and evolutionary insights.
  • Simulates evolutionary processes, producing proteins equivalent to 500 million years of natural evolution within days.

Protein Structure Prediction

Literature Notes - ESM2

less than 1 minute read

Published:

  • Traditional protein prediction models rely heavily on evolutionary information, often requiring computationally expensive multiple sequence alignment (MSA) inputs.
  • ESM2, powered by large language models (LLMs), learns evolutionary patterns directly from raw protein sequences, eliminating MSA requirements and simplifying the computational pipeline.
  • Achieves a ~60x faster inference compared to prior state-of-the-art methods, facilitating studies on vast metagenomic datasets (e.g., MGnify90).

Single-Cell Transcriptomics

Literature Notes - Geneformer

2 minute read

Published:

  • Traditional methods for analyzing single-cell transcriptomics rely on cell-type-specific annotations or perturbation-based studies, which can be resource-intensive and dataset-specific.
  • Geneformer, a transformer-based foundation model, learns transcriptional dynamics directly from large-scale unlabelled single-cell transcriptomes, enabling context-aware gene mapping across diverse cellular states and conditions.
  • Pretrained on Genecorpus-30M (29.9 million single-cell transcriptomes), Geneformer provides a robust framework for a wide range of downstream applications, from dosage sensitivity prediction to in silico gene perturbation analysis.