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.
ChemGrapher uses deep learning to automatically convert chemical compound images into accurate graphs.
Generates natural product-like compounds using GPT models.
DeepSIBA predicts biological effects of chemical structures using graph neural networks.
The paper develops a Gaussian process model for predicting chemical efficacy.
CSLVAE generates large chemical libraries efficiently.
Multimodal deep learning improves toxicity prediction accuracy.
We propose a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). Based on deep and reinforcement learning approaches, ReLeaSE integrates two deep neural networks - generative and predictive - that are trained separately b…
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…
ASLA learns atomic structures using neural networks and reinforcement learning.
Model predicts diverse chemical reactions for target compounds.
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…
Everyday we are exposed to various chemicals via food additives, cleaning and cosmetic products and medicines -- and some of them might be toxic. However testing the toxicity of all existing compounds by biological experiments is neither financially nor logistically feasible. Therefore the government agencies NIH, EPA …
Graph convolutional network (GCN) is generalization of convolutional neural network (CNN) to work with arbitrarily structured graphs. A binary adjacency matrix is commonly used in training a GCN. Recently, the attention mechanism allows the network to learn a dynamic and adaptive aggregation of the neighborhood. We pro…
Hyperbolic volume correlates with chemical properties of fullerenes.
High throughput screening of compounds (chemicals) is an essential part of drug discovery [7], involving thousands to millions of compounds, with the purpose of identifying candidate hits. Most statistical tools, including the industry standard B-score method, work on individual compound plates and do not exploit cross…
Bayesian learning improves reliability of molecular predictions for hit compound discovery.
Capsule Neural Networks classify graphs from categorical features and relationships.
Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compo…
A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used t…
This paper examines data transfer methods to improve off-the-shelf Transformer models for retrosynthesis.
New model uses pretrained biochemical language models to generate drug compounds.
Generative models accelerate chemical design from properties to structures.
Generative model learns to create molecules with multiple properties using interpretable substructures.
Virtual screening (VS) is widely used during computational drug discovery to reduce costs. Chemogenomics-based virtual screening (CGBVS) can be used to predict new compound-protein interactions (CPIs) from known CPI network data using several methods, including machine learning and data mining. Although CGBVS facilitat…
Deep CNN model predicts neuronal cell health from images.
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…
For quantitative structure-property relationship (QSPR) studies in chemoinformatics, it is important to get interpretable relationship between chemical properties and chemical features. However, the predictive power and interpretability of QSPR models are usually two different objectives that are difficult to achieve s…
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…
Proposes SGCN for spatially structured data.
CRYSPNet predicts crystal structures from chemical compositions.
Novel model predicts anticancer compound sensitivity with high accuracy and interpretability.
KANEL combines models for early hit enrichment in virtual screening.
Rank-based Bayesian Optimization improves molecule selection in chemical systems.
Toxicity prediction of chemical compounds is a grand challenge. Lately, it achieved significant progress in accuracy but using a huge set of features, implementing a complex blackbox technique such as a deep neural network, and exploiting enormous computational resources. In this paper, we strongly argue for the models…
Generative model tailors anticancer drugs based on transcriptomic data.
Proposes qPO, a new acquisition strategy for batched Bayesian optimization that maximizes the probability of including the optimum.
Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction from graphs is not a trivial task, and much work has been done in the field of machine learning and data mining. The recent advance…
Improved chemical reaction prediction using augmented NLP models.
Paper explores ML for UV spectra, showing transferability in chemical space.
Statistical detection of a rare class of objects in a two-class classification problem can pose several challenges. Because the class of interest is rare in the training data, there is relatively little information in the known class response labels for model building. At the same time the available explanatory variabl…
Novel RL approach for molecular design using quantum mechanics.
Graph neural networks improve molecular property prediction.
Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications. This task can be formulated as a combinatorial problem, and it takes many hours of human experts to construct, and to evaluate new data. Unsupervised learning me…
A novel deep learning method for chemometric data improves performance over transfer learning.
Proposes semi-supervised feature ranking for handling high-dimensional, unlabeled data.
Improves drug properties using a novel LLM and reinforcement learning.
Manifold methods improve amino acid classification in LIBS spectra.
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several m…