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Results for Predictive coding

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13 papers · ranked by Valyu relevance

01
CchemRxiv23 Oct 2025
Machine Learning Prediction of Enantioselectivity in Asymmetric Catalysis from Small, Curated Datasets: Case Studies in Magnesium-Catalyzed Epoxidation and Thia-Michael Addition
Authors not listed
Theoretical prediction of enantioselectivity for broad range of substrates in a given reaction has long been a formidable challenge, traditionally replaced by labor-intensive screening of multiple conditions. Until recently this remained an unaddressed problem in asymmetric catalysis under data-limited scenarios, yet…
61relevance
02
CchemRxiv2 Dec 2025
Towards Generalizable Data-Driven Pharmacokinetics with Interpretable Neural ODEs
Authors not listed
Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
60relevance
03
CchemRxiv17 Oct 2025
Positivity is All You Need (PAYN): A PU Learning Framework for Yield Prediction in Organic Chemistry
Authors not listed
Accurately predicting chemical reaction yields in silico is a long-standing goal in organic chemistry that, if achieved, would revolutionize synthesis design, op-timization, and discovery. The vast reaction data within scientific literature rep-resents a rich resource for training predictive machine learning models…
58relevance
04
CchemRxiv6 Nov 2025
Honegumi RAG Assistant: An Agentic System for Accelerating Bayesian Optimization Adoption in Experimental Sciences
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
56relevance
05
CchemRxiv13 Oct 2025
From Beakers to Bytes: Educating the Hybrid Chemists — A Chemistry Education Perspective on Integrating Wet and Dry Labs A Call for Hybrid Skills in the Next Generation of Chemist
Authors not listed
Chemistry curricula often separate “wet” experimental work from “dry” computation, yet modern discovery increasingly demands both. This Perspective offers an instructor-ready roadmap to train “hybrid chemists” within existing courses. We distill recent advances in machine learning, automation, and real-time analytics…
56relevance
06
CchemRxiv28 Oct 2025
MCLFormer: A Text-Driven Deep Learning Framework for Predicting CO2 Adsorption in Metal-Organic Frameworks
Authors not listed
Metal–organic frameworks (MOFs) represent a versatile class of porous materials, yet efficiently exploring their vast chemical space for target gas adsorption properties remains a major challenge. MOFid, a text-based encoding of MOF structures, has enabled large-scale data mining using natural language processing (NLP)…
56relevance
07
CchemRxiv6 Jan 2026
Embedding-Based Applicability Domain for Graph Neural Networks: A Comprehensive Benchmark on Molecular Property Prediction
Authors not listed
Graph Neural Networks (GNNs) are powerful tools for molecular property prediction, but they are not magic. When applied to molecules unlike their training data, they produce unreliable predictions that are difficult to detect. The Applicability Domain (AD) concept addresses this by defining regions of chemical space…
53relevance
08
CchemRxiv30 Oct 2025
Causal Descriptors in QSAR: Deconfounding High-Dimensional Molecular Features via Double Machine Learning and Hypothesis Testing
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
53relevance
09
CchemRxiv18 Nov 2025
A Multidimensional In Silico Guidance to Map and Optimize Competing Nitroso Reactions
Authors not listed
We present an updated version of a priori computational intelligence, a methodology that integrates semi-empirical Quantum Mechanics calculations with supervised machine learning to predict optimal reaction conditions without prior extensive experimental work. First, the synergy between semi-empirical calculations and…
53relevance
10
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…
53relevance
11
CchemRxiv13 Jan 2026
A Rigorous Statistical Benchmark of the Tsetlin Machine: A Novel Rule-Based Method Exceeding SOTA for QSAR Prioritisation
Authors not listed
Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
51relevance
12
CchemRxiv18 Dec 2025
Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
51relevance
13
CchemRxiv7 Jan 2026
Mechanism-aware multimodal learning for organ-specific drug toxicity prediction through adaptive modality gating and text-neutralization stress testing
Authors not listed
Early prediction of drug-induced organ toxicity remains a major bottleneck in drug discovery and clinical pharmacotherapy. Most data-driven toxicity models behave as endpoint predictors: they output a label but provide limited transparency about why a compound is risky or which evidence channel dominated the decision.…
50relevance

Results for Predictive coding

Machine Learning Prediction of Enantioselectivity in Asymmetric Catalysis from Small, Curated Datasets: Case Studies in Magnesium-Catalyzed Epoxidation and Thia-Michael Addition

Towards Generalizable Data-Driven Pharmacokinetics with Interpretable Neural ODEs

Positivity is All You Need (PAYN): A PU Learning Framework for Yield Prediction in Organic Chemistry

Honegumi RAG Assistant: An Agentic System for Accelerating Bayesian Optimization Adoption in Experimental Sciences

From Beakers to Bytes: Educating the Hybrid Chemists — A Chemistry Education Perspective on Integrating Wet and Dry Labs A Call for Hybrid Skills in the Next Generation of Chemist

MCLFormer: A Text-Driven Deep Learning Framework for Predicting CO2 Adsorption in Metal-Organic Frameworks

Embedding-Based Applicability Domain for Graph Neural Networks: A Comprehensive Benchmark on Molecular Property Prediction

Causal Descriptors in QSAR: Deconfounding High-Dimensional Molecular Features via Double Machine Learning and Hypothesis Testing

A Multidimensional In Silico Guidance to Map and Optimize Competing Nitroso Reactions

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

A Rigorous Statistical Benchmark of the Tsetlin Machine: A Novel Rule-Based Method Exceeding SOTA for QSAR Prioritisation

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

Mechanism-aware multimodal learning for organ-specific drug toxicity prediction through adaptive modality gating and text-neutralization stress testing

Machine Learning Prediction of Enantioselectivity in Asymmetric Catalysis from Small, Curated Datasets: Case Studies in Magnesium-Catalyzed Epoxidation and Thia-Michael Addition

Towards Generalizable Data-Driven Pharmacokinetics with Interpretable Neural ODEs

Positivity is All You Need (PAYN): A PU Learning Framework for Yield Prediction in Organic Chemistry

Honegumi RAG Assistant: An Agentic System for Accelerating Bayesian Optimization Adoption in Experimental Sciences

From Beakers to Bytes: Educating the Hybrid Chemists — A Chemistry Education Perspective on Integrating Wet and Dry Labs A Call for Hybrid Skills in the Next Generation of Chemist

MCLFormer: A Text-Driven Deep Learning Framework for Predicting CO2 Adsorption in Metal-Organic Frameworks

Embedding-Based Applicability Domain for Graph Neural Networks: A Comprehensive Benchmark on Molecular Property Prediction

Causal Descriptors in QSAR: Deconfounding High-Dimensional Molecular Features via Double Machine Learning and Hypothesis Testing

A Multidimensional In Silico Guidance to Map and Optimize Competing Nitroso Reactions

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

A Rigorous Statistical Benchmark of the Tsetlin Machine: A Novel Rule-Based Method Exceeding SOTA for QSAR Prioritisation

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

Mechanism-aware multimodal learning for organ-specific drug toxicity prediction through adaptive modality gating and text-neutralization stress testing