Img2Mol: Accurate SMILES Recognition from Molecular Graphical Depictions
Clevert et al.'s two-stage CNN approach for converting molecular images to SMILES using CDDD embeddings and extensive …...
Deep-Learning
MolNexTR: A Generalized Deep Learning Model for Molecular Image Recognition
Chen et al.'s dual-stream encoder approach for robust molecular structure recognition from diverse real-world images …...
MolParser-7M and WildMol Datasets for Robust Chemical Structure Recognition
MolParser-7M is a 7.7M-pair dataset for molecule-to-text conversion, featuring real-world images and complex structures …
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild
Fang et al.'s method for converting molecular structure images from scientific documents into machine-readable formats …...
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 …...
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Luo et al. introduce SPHNet, using adaptive sparsity to dramatically improve SE(3)-equivariant Hamiltonian prediction …...
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 …...