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Results for Classification accuracy

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16 papers · ranked by Valyu relevance

01
CchemRxiv23 Dec 2025
Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
77relevance
02
CchemRxiv10 Oct 2022
From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization
Saer Samanipour, Jake O'Brien, Malcolm Reid, Kevin Thomas + 1 more
The European Chemicals Agency (ECHA) and US Environmental Protection Agency (EPA) have listed approximately 800k chemicals that must be further investigated for their potential environmental and/or human health risk. A significant number of these chemicals have large enough global volumes of consumption (e.g.…
72relevance
03
CchemRxiv21 Jun 2022
From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization
Saer Samanipour, Jake O'Brien, Malcolm Reid, Kevin Thomas + 1 more
The European and US chemical agencies have listed approximately 800k chemicals where knowledge on potential risks to human health and the environment are lacking. Filling these data gaps experimentally is impossible so in-silico approaches and prediction are essential. Many existing models are however limited by…
72relevance
04
CchemRxiv17 Dec 2025
Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
71relevance
05
CchemRxiv5 Jun 2023
Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?
Esteban Bertsch Aguilar, Sebastián Suñer Sánchez, Silvana Pinheiro, William J. Zamora Ramírez
- 1. 1. CBio3 Laboratory, School of Chemistry, University of Costa Rica, San Pedro, San José, Costa Rica - 2. 2. Laboratory of Computational Toxicology and Artificial Intelligence (LaToxCIA), Biological Testing Laboratory (LEBi), University of Costa Rica, San Pedro, San José, Costa Rica - 3. 3. Advanced Computing Lab…
71relevance
06
CchemRxiv26 Dec 2023
One size does not fit all: revising traditional paradigms for QSAR-based virtual screenings.
James Wellnitz, Sankalp Jain, Joshua Hochuli, Travis Maxfield + 3 more
Traditional best practices for Quantitative Structure Activity Relationship (QSAR) modeling recommend dataset balancing and balanced accuracy (BA) as the key desired objective of model development. This study challenges the conventional norms by recommending the use of models with the highest positive predictive value…
71relevance
07
CchemRxiv26 Apr 2023
Inferring the nominal molecular mass of an analyte from its electron ionization mass spectrum
Arun Moorthy, Anthony Kearsley, William Mallard, William Wallace + 1 more
The performance of three algorithms for predicting nominal molecular mass from an analyte’s electron ionization mass spectrum are presented. The Peak Interpretation Method (PIM) attempts to quantify the likelihood that a molecular ion peak is contained in the mass spectrum, whereas the Simple Search Hitlist Method…
69relevance
08
CchemRxiv7 Oct 2022
From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization
Saer Samanipour, Jake O'Brien, Malcolm Reid, Kevin Thomas + 1 more
The European Chemicals Agency (ECHA) and US Environmental Protection Agency (EPA) have listed approximately 800k chemicals that must be further investigated for their potential environmental and/or human health risk. A significant number of these chemicals have large enough global volumes of consumption (e.g.…
67relevance
09
CchemRxiv24 Jan 2020
Spectral Deep Learning for Prediction and Prospective Validation of Functional Groups
Jonathan Fine, Anand Rasjashekar, Krupal P. Jethava, Gaurav Chopra
State-of-the-art identification of the functional groups present in an unknown chemical entity requires expertise of a skilled spectroscopist to analyse and interpret Fourier Transform Infra-Red (FTIR), Mass Spectroscopy (MS) and/or Nuclear Magnetic Resonance (NMR) data. This process can be time-consuming and…
65relevance
10
CchemRxiv27 Mar 2024
Machine Learning in Chemistry
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
63relevance
11
CchemRxiv12 Dec 2023
Data Valuation: A novel approach for analyzing high throughput screen data using machine learning
Joshua Hesse, Davide Boldini, Stephan Sieber
In the rapidly evolving field of drug discovery, High Throughput Screening (HTS) is a pivotal technique for identifying promising compounds. Despite its wide usage, the primary challenge remains in efficiently sifting through vast chemical libraries to discern true bioactive compounds from false positives. This study…
62relevance
12
CchemRxiv24 Sep 2025
Towards robust databases: an ensemble-based workflow for error detection applied to chemical data
Authors not listed
This study presents a validation and refinement of the “yellow cards” error detection workflow that can be applied to any property connected to molecular structure. In our implementation the workflow employed 5 predictive models with each assigning a “yellow card” to 5% of the entries with worst prediction accuracy.…
61relevance
13
CchemRxiv8 Feb 2023
Characterizing Uncertainty in Machine Learning for Chemistry
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
61relevance
14
CchemRxiv29 Sep 2025
TabPFN Opens New Avenues for Small-Data Tabular Learning in Drug Discovery
Authors not listed
Early-stage drug discovery often suffers from data scarcity and out-of-distribution (OOD) shifts, which constrain the reliability of predictive models. While deep learning has advanced representation learning from molecular and biological data, tabular modeling remains indispensable, particularly in small-sample and…
60relevance
15
CchemRxiv31 Dec 2019
Graph Based Machine Learning Interprets Diagnostic Isomer-Selective Ion-Molecule Reactions in Tandem Mass Spectrometry
Jonathan A Fine, Judy Kuan-Yu Liu, Armen Beck, Kawthar Alzarieni + 4 more
Diagnostic ion-molecule reactions using tandem mass spectrometry can differentiate between isomeric compounds unlike a popular collision-activated dissociation methodology for the identification of previously unknown mixtures. Selected neutral reagents, such as 2-methoxypropene (MOP) are introduced into an ion trap…
59relevance
16
CchemRxiv8 Sep 2023
Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen
With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet…
53relevance

Results for Classification accuracy

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

One size does not fit all: revising traditional paradigms for QSAR-based virtual screenings.

Inferring the nominal molecular mass of an analyte from its electron ionization mass spectrum

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization

Spectral Deep Learning for Prediction and Prospective Validation of Functional Groups

Machine Learning in Chemistry

Data Valuation: A novel approach for analyzing high throughput screen data using machine learning

Towards robust databases: an ensemble-based workflow for error detection applied to chemical data

Characterizing Uncertainty in Machine Learning for Chemistry

TabPFN Opens New Avenues for Small-Data Tabular Learning in Drug Discovery

Graph Based Machine Learning Interprets Diagnostic Isomer-Selective Ion-Molecule Reactions in Tandem Mass Spectrometry

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

One size does not fit all: revising traditional paradigms for QSAR-based virtual screenings.

Inferring the nominal molecular mass of an analyte from its electron ionization mass spectrum

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization

Spectral Deep Learning for Prediction and Prospective Validation of Functional Groups

Machine Learning in Chemistry

Data Valuation: A novel approach for analyzing high throughput screen data using machine learning

Towards robust databases: an ensemble-based workflow for error detection applied to chemical data

Characterizing Uncertainty in Machine Learning for Chemistry

TabPFN Opens New Avenues for Small-Data Tabular Learning in Drug Discovery

Graph Based Machine Learning Interprets Diagnostic Isomer-Selective Ion-Molecule Reactions in Tandem Mass Spectrometry

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets