A 2025 Vision-Language Model for OCSR that uses graph traversal chain-of-thought reasoning to handle chemical abbreviations (Ph, Et, etc.) that break existing systems, achieving state-of-the-art performance by training on data where abbreviations are preserved.
SubGrapher introduces a visual fingerprinting approach to Optical Chemical Structure Recognition that detects functional groups directly from images, enabling chemical database searches without full structure reconstruction and handling complex patent images including Markush structures.
Discover how OCSR technology bridges the gap between molecular images and machine-readable data, evolving from rule-based systems to modern deep learning models for chemical knowledge extraction.
A 2024 deep learning system for optical chemical structure recognition designed specifically for biomedical literature mining, using ResNet-Transformer architecture to handle challenging conditions including low-resolution images, noise, distortions, and even hand-drawn molecular diagrams from scientific documents.
The MolParser project introduces two key datasets: MolParser-7M, the largest training dataset for Optical Chemical Structure Recognition (OCSR) with 7.7M pairs of images and E-SMILES strings, and WildMol, a new 20k-sample benchmark for evaluating models on challenging real-world data. The training data uniquely combines millions of diverse synthetic molecules with 400,000 manually annotated in-the-wild samples to significantly enhance model robustness.
An in-depth overview of SELFIES, the 100% robust molecular string representation designed to overcome SMILES limitations in machine learning, where every possible string (even random ones) decodes to a valid molecule through local operations, customizable valence rules, and graph-based internal representations.
Shannon’s foundational 1949 paper establishing the mathematical framework for modern information theory, defining channel capacity as the fundamental limit for reliable communication over noisy channels and introducing the sampling theorem (Nyquist-Shannon) that underpins all digital signal processing.
Step-by-step implementation of the classic Müller-Brown potential in PyTorch, with performance comparisons between analytical and automatic differentiation approaches for molecular dynamics and machine learning applications.
ICLR 2025 paper introducing DenoiseVAE, which learns adaptive, atom-specific noise distributions through a VAE framework to improve denoising-based pre-training for molecular force field prediction, outperforming fixed Gaussian noise approaches on quantum chemistry benchmarks.
ICML 2025 paper introducing SpaceFormer, a Transformer architecture that challenges the atom-centric paradigm by modeling the continuous 3D space surrounding molecules using adaptive multi-resolution grids, achieving state-of-the-art performance on quantum property prediction benchmarks.
ICML 2025 analysis rigorously quantifying when non-conservative force models (which predict forces directly) fail in molecular dynamics, demonstrating simulation instabilities and proposing hybrid architectures that capture speed benefits without sacrificing physical correctness.
ICML 2025 methodological paper introducing SPHNet, which uses adaptive network sparsification to overcome the computational bottleneck of tensor products in SE(3)-equivariant networks, achieving up to 7x speedup and 75% memory reduction on DFT Hamiltonian prediction tasks.