A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
NLP techniques improve drug discovery by analyzing chemical and protein text.
Optimizes molecular generation for chemist preferences.
Generates natural product-like compounds using GPT models.
Identification of high affinity drug-target interactions is a major research question in drug discovery. Proteins are generally represented by their structures or sequences. However, structures are available only for a small subset of biomolecules and sequence similarity is not always correlated with functional similar…
ChemCrow enhances LLMs for chemistry tasks, automating complex chemical processes.
Improved chemical reaction prediction using augmented NLP models.
MoleculeSTM learns from molecule structures and texts for better drug design.
New model uses pretrained biochemical language models to generate drug compounds.
Improves drug properties using a novel LLM and reinforcement learning.
The paper develops a Gaussian process model for predicting chemical efficacy.
Enhances drug discovery models by understanding human language.
GraphAF generates chemically valid molecules efficiently and accurately.
MEGAN models chemical reactions as graph edits, improving synthesis planning.
MoFlow generates chemically valid molecular graphs from latent representations.
Text classification on drug SMILES strings yields competitive drug type classification results.
Researchers derive the chemical potential equation for ideal agent systems.
We present chemlambda (or the chemical concrete machine), an artificial chemistry with the following properties: (a) is Turing complete, (b) has a model of decentralized, distributed computing associated to it, (c) works at the level of individual (artificial) molecules, subject of reversible, but otherwise determinist…
With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers. Here, we describe interpretation techniques for atomistic neural networks on the example of …
In this work, we present an application of Locally Interpretable Machine-Agnostic Explanations to 2-D chemical structures. Using this framework we are able to provide a structural interpretation for an existing black-box model for classifying biologically produced fuel compounds with regard to Research Octane Number. T…
Generating molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. However, from visual inspection, it often appears that generated samples lack diversity. In this paper, we quantify this internal chemical diversity, and we raise the …
FlowLLM uses LLMs and flow matching to efficiently generate novel materials.
DeepSIBA predicts biological effects of chemical structures using graph neural networks.
Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity of the chemical mechanisms: systems of coupled differential equations representing atmospheric chemistry. Here we investigate the potential…
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automat…
A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials datab…
Geometric modeling for human food and chemical sensitivities.
This review discusses challenges and solutions for AI in chemical engineering.
Framework separates chemical and structural contributions to aqueous solubility.
Chemical autoencoders are attractive models as they combine chemical space navigation with possibilities for de-novo molecule generation in areas of interest. This enables them to produce focused chemical libraries around a single lead compound for employment early in a drug discovery project. Here it is shown that the…
ChemGrapher uses deep learning to automatically convert chemical compound images into accurate graphs.
Chemical structure elucidation is a serious bottleneck in analytical chemistry today. We address the problem of identifying an unknown chemical threat given its mass spectrum and its chemical formula, a task which might take well trained chemists several days to complete. Given a chemical formula, there could be over a…
A framework separates chemical and structural contributions to aqueous solubility.
During reactive transport modeling, the computational cost associated with chemical reaction calculations is often 10-100 times higher than that of transport calculations. Most of these costs results from chemical equilibrium calculations that are performed at least once in every mesh cell and at every time step of the…
AI helps in drug discovery with understandable explanations.
Bio-oil molecule assessment is essential for the sustainable development of chemicals and transportation fuels. These oxygenated molecules have adequate carbon, hydrogen, and oxygen atoms that can be used for developing new value-added molecules (chemicals or transportation fuels). One motivation for our study stems fr…
Analyzes Indian chemical industry post-Covid.
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable de…
Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important examples include gene expression and enzymatic processes in living cells. Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the Chemical Master Equation. Despite its simple structur…
A new method uses active learning to improve chemical simulation efficiency.
DeepVARMA predicts chemical industry index trends using LSTM and VARMAX models.
CRNN discovers chemical reaction pathways from data.
We briefly review recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals. For detecting chemicals of known spectrum, we extend the technique of using a single subspace for modeling the background to a "mixture of s…
Chemical networks outperform spiking neural networks in classification tasks.
Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. The number of fragments required for chemical accuracy is still prohibitively expensive for ab-initio MBE to compete with force fi…
Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures. We introduce MolGAN, an imp…
Automated method creates compact chemical models from detailed ones, reducing complexity and improving accuracy.
Rank-based Bayesian Optimization improves molecule selection in chemical systems.
Review of automation's role in chemical discovery, emphasizing future challenges.