arXiv research

PADME predicts drug-target interaction strengths using deep learning.

Simple machine learning models achieve high accuracy in toxicity prediction.

Paper proposes a framework to predict therapeutic properties of compounds.

The understanding of the type of inhibitory interaction plays an important role in drug design. Therefore, researchers are interested to know whether a drug has competitive or non-competitive interaction to particular protein targets. Method: to analyze the interaction types we propose factorization method Macau which …

QSAR models struggle to predict activity cliffs, but graph isomorphism features improve AC-sensitivity.

A defining feature of non-stationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent parameters. To model…

Proposes semi-supervised feature ranking for handling high-dimensional, unlabeled data.

The paper develops a Gaussian process model for predicting chemical efficacy.

Improves molecular activity prediction using graph convolutional neural networks considering graph distances.

Multimodal deep learning improves toxicity prediction accuracy.

Study estimates Medallion's compounded return before fees at 31.8%.

Characterizes measures preserving compound mixed renewal process properties.

This chapter is an attempt to present a mathematical theory of compound fractional Poisson processes. The chapter begins with the characterization of a well-known Lévy process: The compound Poisson process. The semi-Markov extension of the compound Poisson process naturally leads to the compound fractional Poisson proc…

Study develops advanced models to forecast complex LOB data.

Expanding spoken language understanding to handle complex entities and intents.

Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov Transition Fields (MTF). This enables the use of techniques from computer vision for time serie…

Study analyzes risk management in Aave and Compound lending protocols, finding v3 better than v2.

Generates natural product-like compounds using GPT models.

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…

Develops an efficient method for compound option valuation.

Analyzes compound interest with constant payments and interest rate.

In this paper, we introduce a new model for the risk process based on general compound Hawkes process (GCHP) for the arrival of claims. We call it risk model based on general compound Hawkes process (RMGCHP). The Law of Large Numbers (LLN) and the Functional Central Limit Theorem (FCLT) are proved. We also study the ma…

Capsule Neural Networks classify graphs from categorical features and relationships.

Semi-supervised learning improves QSAR model predictions for novel compounds.

Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized comp…

Compound Finance optimizes risk metrics for V3 protocol using Chainrisk simulations.

Financial investment returns lead to growing wealth inequality.

STMT predicts compounds in unknown areas with trend reflection.

New Riemannian geometry for Compound Gaussian distributions applied to efficient change detection.

This study compares decentralized banks and finds some lack decentralization.

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…

ChemGrapher uses deep learning to automatically convert chemical compound images into accurate graphs.

This study deals with the problem of pricing compound options when the underlying asset follows a mixed fractional Brownian motion with jumps. An analytic formula for compound options is derived under the risk neutral measure. Then, these results are applied to value extendible options. Moreover, some special cases of …

Novel model predicts anticancer compound sensitivity with high accuracy and interpretability.

The study improves compound selection in in silico screening by focusing on model's ability to predict desirable outcomes.

A new method solves complex financial problems using deep learning.

Ethereum's Pectra upgrade introduces 0x02 compounding validators, offering higher stake and potential APR uplifts.

The paper develops generalization bounds for deep compound Gaussian neural networks.

New MGCPP model for order flow in financial markets.

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…

Study predicts shear stress in compound channels using data mining and machine learning.

The paper introduces ESG valuation in option pricing using binomial trees.

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 …

We propose an analytical approach to the computation of tail probabilities of compound distributions whose individual components have heavy tails. Our approach is based on the contour integration method, and gives rise to a representation of the tail probability of a compound distribution in the form of a rapidly conve…

Study shows convergence rates for BSDEs approximated by compound Poisson processes.

Bayesian learning improves reliability of molecular predictions for hit compound discovery.

A multi-step model reduces compounding errors in reinforcement learning.

New approach tackles open compound domain adaptation without clear domain labels.