Converting SELFIES Strings to 2D Molecular Images
Learn how to visualize SELFIES molecular representations and explore their unique advantages through random sampling, …
Machine-Learning
SELFIES (Self-Referencing Embedded Strings)
SELFIES is a 100% robust string-based representation for chemical molecules, designed for machine learning applications …...
Implementing the Müller-Brown Potential in PyTorch
Guide to implementing the Müller-Brown potential in PyTorch, comparing analytical vs automatic differentiation with …
DenoiseVAE: Learning Molecule-Adaptive Noise Distributions for Denoising-based 3D Molecular Pre-training
Liu et al.'s ICLR 2025 paper introducing DenoiseVAE, which learns adaptive, atom-specific noise for better molecular …...
Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling
Lu et al. introduce SpaceFormer, a Transformer that models entire 3D molecular space—not just atoms—for superior …...
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
Fu et al. propose energy conservation as a key MLIP diagnostic and introduce eSEN, bridging test accuracy and real …...
The Dark Side of the Forces: Assessing Non-Conservative Force Models for Atomistic Machine Learning
Bigi et al. critique non-conservative force models in ML potentials, showing their simulation failures and proposing …...
A Contrastive Learning Approach for Training Variational Autoencoder Priors
Dai et al.'s NeurIPS 2021 paper introducing Noise Contrastive Priors (NCPs) to address VAE's 'prior hole' problem with …...
GEOM Dataset: 3D Molecular Conformer Generation
Learn how GEOM transforms 2D molecular graphs into dynamic 3D conformer ensembles for molecular machine learning …
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Weiler et al.'s NeurIPS 2018 paper introducing SE(3)-equivariant CNNs for volumetric data using group theory and …...
Invalid SMILES are Beneficial Rather than Detrimental to Chemical Language Models
Skinnider's 2024 Nature Machine Intelligence paper demonstrates that the ability to generate invalid SMILES is actually …...
Can You Hear the Shape of a Molecule? (Part Three)
Supervised learning reveals hidden eigenvalue patterns that clustering missed, testing k-NN and logistic regression on …