A really interesting preprint caught my attention from Connor Coley’s group at MIT. ShEPhERD diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design https://arxiv.org/abs/2411.04130v1 …
Tag: machine learning
A new Apple preprint has appeared on Arxiv. https://arxiv.org/pdf/2403.20329.pdf Reference resolution is an important problem, one that is essential to understand and success- fully handle
I suspect many people have been anticipating this, whilst AlphaFold was a great step forward in predicting protein 3D structure it did have significant limitations.
I know a few folks have been using this, good to see this publication DOI. Chemprop implements the D-MPNN architecture and offers simple, easy, and
We are starting to see papers coming from Apple that highlight their efforts in the machine learning/artificial intelligence area. Recently we have seen MLX a
Cambridge Open Engage is the collaborative platform to upload, share and advance early and open research and a recent post caught my eye. Molecular set
I just stumbled across this repository on GitHub, MLX: An array framework for Apple silicon https://github.com/ml-explore/mlx MLX is a NumPy-like array framework designed for efficient
Just stumbled across this blog on cheminformatics, machine learning (ML) and data science projects in drug discovery. Lots of useful code! Data in Life https://jhylin.github.io/Data_in_life_blog/
I just stumbled across this and I thought I’d share it. Seamlessly integrate powerful language models like ChatGPT into scikit-learn for enhanced text analysis tasks.
In a recent post Pat Walters highlighted the use of molfeat in a google colab notebook https://colab.research.google.com/github/PatWalters/practicalcheminformaticstutorials/blob/main/mlmodels/QSARin8lines.ipynb. I thought I’d also mention other tools available from