This paper replaces discrete network layers with continuous ordinary differential equations (ODEs), allowing for adaptive computation depth and constant memory cost during training via the adjoint sensitivity method. It introduces Continuous Normalizing Flows and latent ODEs for time-series.
This paper provides a rigorous probabilistic foundation for Denoising Autoencoders by proving they are mathematically equivalent to Score Matching on a kernel-smoothed data distribution. It derives a specific energy function for DAEs and justifies the use of tied weights.
This paper unifies previous score-based methods (SMLD and DDPM) under a continuous-time SDE framework. It introduces Predictor-Corrector samplers for improved generation and Probability Flow ODEs for near-exact likelihood computation, setting new records on CIFAR-10.
ChemDFM-X is a multimodal chemical foundation model that integrates five non-text modalities (2D graphs, 3D conformations, images, MS2 spectra, IR spectra) into a single LLM decoder. It overcomes data scarcity by generating a 7.6M instruction-tuning dataset through approximate calculations and model predictions, establishing strong baseline performance across multiple modalities.
This paper introduces DynamicFlow, a full-atom stochastic flow matching model that simultaneously generates ligand molecules and transforms protein pockets from apo to holo states. It also contributes a new dataset of MD-simulated apo-holo pairs derived from MISATO.
Deep dive into 24 image-to-sequence OCSR methods (2019-2025), comparing encoder-decoder architectures, molecular string representations, training scale, and hardware requirements.
InstructMol integrates a pre-trained molecular graph encoder (MoleculeSTM) with a Vicuna-7B LLM using a linear projector. It employs a two-stage training process (alignment pre-training followed by task-specific instruction tuning with LoRA) to excel at property prediction, description generation, and reaction analysis.
InvMSAFold replaces autoregressive decoding with a Potts model parameter generator, enabling diverse protein sequence sampling orders of magnitude faster than ESM-IF1.
MOFFlow is the first deep generative model tailored for Metal-Organic Framework (MOF) structure prediction. It utilizes Riemannian flow matching on SE(3) to assemble rigid building blocks (metal nodes and organic linkers), achieving higher accuracy and scalability than atom-based methods on large systems.
This paper presents a multimodal search system that facilitates passage-level retrieval of chemical reactions and molecular structures by linking diagrams, text, and reaction records extracted from scientific PDFs.
STOUT V2.0 uses Transformers trained on ~1 billion SMILES-IUPAC pairs to accurately translate chemical structures into systematic names (and vice-versa), outperforming its RNN predecessor.
STOUT (SMILES-TO-IUPAC-name translator) uses neural machine translation to convert chemical line notations to IUPAC names and vice versa, achieving ~90% BLEU score. It addresses the lack of open-source tools for algorithmic IUPAC naming.