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JCIM & JCTC Journals

@JCIM_JCTC

Tweets from Journal of Chemical Information and Modeling (JCIM) and Journal of Chemical Theory and Computation (JCTC) from @ACSPublications

 Washington, DC
pubs.acs.org/
Joined February 2015
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📢 Check the new Volume 66 Issue 186 of #JCIM pubs.acs.org/toc/jcisd8/66/1…
Image from JCIM & JCTC Journals (@JCIM_JCTC)
Image from JCIM & JCTC Journals (@JCIM_JCTC)
Image from JCIM & JCTC Journals (@JCIM_JCTC)
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AquaTox-Predictor: An MMoE-Enhanced Multimodal Deep Learning Framework for Aquatic Ecotoxicity Prediction pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #ChemicalInformation
AquaTox-Predictor: An MMoE-Enhanced Multimodal Deep Learning Framework for Aquatic Ecotoxicity...

AquaTox-Predictor: An MMoE-Enhanced Multimodal Deep Learning Framework for Aquatic Ecotoxicity...

Abstract. As the concentration of compounds in aquatic environments rises, the degradation of aquatic habitats underscores the increasing importance of stu

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SynOmega: Simplifying Retrosynthesis for Efficient Synthesizability Scoring pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #ChemicalInformation
SynOmega: Simplifying Retrosynthesis for Efficient Synthesizability Scoring

SynOmega: Simplifying Retrosynthesis for Efficient Synthesizability Scoring

Abstract. Retrosynthesis-based synthesizability scoring triages molecules from generative design but is expensive: every score requires a multi-step search

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Assay-Level Modeling and External Validation of Antibody Developability Using Physicochemical Descriptors and Sequence Embeddings pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #ChemicalInformation
Assay-Level Modeling and External Validation of Antibody Developability Using Physicochemical...

Assay-Level Modeling and External Validation of Antibody Developability Using Physicochemical...

Abstract. Antibody developability is inherently multidimensional, involving thermal stability, aggregation and hydrophobicity, nonspecific interactions, an

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Navigating Large Chemical Spaces Using Graph Theory and Integer Programming pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #ChemicalInformation
Navigating Large Chemical Spaces Using Graph Theory and Integer Programming

Navigating Large Chemical Spaces Using Graph Theory and Integer Programming

Abstract. Navigating and analyzing large chemical spaces are necessary to accelerate the design and discovery of new molecules and chemical processes. In t

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The Scope Concept: Defined Chemical Space for Identifying Ligand-Efficient, Tractable Hits Applied to Drug Discovery Targets Trypanosomal CLK1, 14–3–3/Estrogen Receptor-α, and SARS-CoV-2 Mpro pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #ChemicalInformation
The Scope Concept: Defined Chemical Space for Identifying Ligand-Efficient, Tractable Hits Applied...

The Scope Concept: Defined Chemical Space for Identifying Ligand-Efficient, Tractable Hits Applied...

Abstract. Scope is a hit-finding concept that explores a chemical space in between fragments and fully elaborated molecules from traditional low molecular

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Extending Partition Coefficient Predictions from Key Solvent Systems to Biological and Environmental Systems via Linear Solvation Energy Relationship pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #ChemicalInformation
Extending Partition Coefficient Predictions from Key Solvent Systems to Biological and Environmen...

Extending Partition Coefficient Predictions from Key Solvent Systems to Biological and Environmen...

Abstract. Accurate prediction of equilibrium partition coefficients of organic compounds in biological and environmental media is critical for evaluating t

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KRstereo: Predicting β-Hydroxy Stereochemistry in Polyketides Using Protein Language Models pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
KRstereo: Predicting β-Hydroxy Stereochemistry in Polyketides Using Protein Language Models

KRstereo: Predicting β-Hydroxy Stereochemistry in Polyketides Using Protein Language Models

Abstract. Polyketides are a major class of bioactive natural products whose activities are often determined by the stereochemistry of β-hydroxyl groups. In

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JCIM & JCTC Journals retweeted
Kuntal Ghosh (@kuntalg259) profile picture
Kuntal Ghosh@kuntalg259Oct 25
A little late to post, but excited to introduce NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment @UChiChemistry @JCIM_JCTC @VothGroup doi.org/10.1021/acs.jctc.6c0…
NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechan...

NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechan...

Abstract. Neural network potentials (NNPs), or neural network-based force fields, are gaining widespread attention for their ability to model complex chemi

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Mechanism-Guided Antimicrobial Peptide Design through Membrane-Surface Fingerprinting and Graph Diffusion pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
Mechanism-Guided Antimicrobial Peptide Design through Membrane-Surface Fingerprinting and Graph...

Mechanism-Guided Antimicrobial Peptide Design through Membrane-Surface Fingerprinting and Graph...

Abstract. Antimicrobial peptides (AMPs) are promising antibiotic alternatives, but current AI-based discovery often relies on sequence labels and lacks exp

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A Task-Adaptive Multimodal Pretrained Framework for Antibiotic Virtual Screening with Joint Activity and Cytotoxicity Prediction pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
A Task-Adaptive Multimodal Pretrained Framework for Antibiotic Virtual Screening with Joint...

A Task-Adaptive Multimodal Pretrained Framework for Antibiotic Virtual Screening with Joint...

Abstract. Antimicrobial resistance (AMR) poses a growing threat to global public health, particularly for priority pathogens such as Staphylococcus aureus

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Evolutionary Weighted Hodge Laplacian Descriptors for rs-fMRI Brain Networks pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
Evolutionary Weighted Hodge Laplacian Descriptors for rs-fMRI Brain Networks

Evolutionary Weighted Hodge Laplacian Descriptors for rs-fMRI Brain Networks

Abstract. Complex biological networks often exhibit multiscale organization that is not fully characterized by pairwise edge weights or fixed graph summari

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Persistent Local Laplacian Prediction of Protein–Ligand Binding Affinities pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
Persistent Local Laplacian Prediction of Protein–Ligand Binding Affinities

Persistent Local Laplacian Prediction of Protein–Ligand Binding Affinities

Abstract. Accurate prediction of protein–ligand binding affinity remains a central challenge in structure-based drug discovery. The effectiveness of machin

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Systematic Evaluation of Graph Neural Networks for Ligand-Based Virtual Screening on ChEMBL Datasets pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
Systematic Evaluation of Graph Neural Networks for Ligand-Based Virtual Screening on ChEMBL Datasets

Systematic Evaluation of Graph Neural Networks for Ligand-Based Virtual Screening on ChEMBL Datasets

Abstract. The performance of target-specific, ligand-based virtual screening models is strongly influenced by dataset characteristics, including data avail

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Ranking-Based Surrogate Modeling for Bayesian Optimization under Small-Data Conditions pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
Ranking-Based Surrogate Modeling for Bayesian Optimization under Small-Data Conditions

Ranking-Based Surrogate Modeling for Bayesian Optimization under Small-Data Conditions

Abstract. Bayesian optimization (BO) is widely used for reaction optimization, but under small-data conditions, regression of absolute objective values may

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Adaptive Data Splitting Strategy to Enhance Knowledge Learning for Phosphorescence Lifetime Prediction pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
Adaptive Data Splitting Strategy to Enhance Knowledge Learning for Phosphorescence Lifetime...

Adaptive Data Splitting Strategy to Enhance Knowledge Learning for Phosphorescence Lifetime...

Abstract. Room-temperature phosphorescence (RTP) lifetime is a critical property for optoelectronic and bioimaging applications, yet its experimental measu

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KinEAGER: An Evidence-Aware Multitask Model with Calibrated Uncertainty for Enzyme Kinetics Prediction pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
KinEAGER: An Evidence-Aware Multitask Model with Calibrated Uncertainty for Enzyme Kinetics...

KinEAGER: An Evidence-Aware Multitask Model with Calibrated Uncertainty for Enzyme Kinetics...

Abstract. Public enzyme-kinetics records have labels that are incomplete across tasks, assay conditions that vary across sources, and structure-associated

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MultiRSF: A Deep Learning Approach for Predicting RNA-Small-Molecule Binding Sites Using Surface Characteristics pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
MultiRSF: A Deep Learning Approach for Predicting RNA-Small-Molecule Binding Sites Using Surface...

MultiRSF: A Deep Learning Approach for Predicting RNA-Small-Molecule Binding Sites Using Surface...

Abstract. Identification of RNA-small-molecule binding sites is a critical first step in RNA-targeted drug discovery. Although several machine learning met

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MMGNN: Multilevel, Multicolor Graph Neural Networks for Molecular Property Prediction pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
MMGNN: Multilevel, Multicolor Graph Neural Networks for Molecular Property Prediction

MMGNN: Multilevel, Multicolor Graph Neural Networks for Molecular Property Prediction

Abstract. Molecular message-passing neural networks commonly propagate chemically diverse interactions through a single graph, which may mix interaction-sp

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ActiveFusion: Fused Representations Improve Active Learning for Molecular Property Prediction pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
ActiveFusion: Fused Representations Improve Active Learning for Molecular Property Prediction

ActiveFusion: Fused Representations Improve Active Learning for Molecular Property Prediction

Abstract. Active learning provides an efficient strategy for molecular property prediction by iteratively prioritizing compounds for experimental evaluatio

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ProAlign-DDI: A Zero-Shot Drug–Drug Interaction Event Prediction Framework with Prototype Alignment pubs.acs.org/doi/10.1021/acs… #JCIM Vol66 Issue18 #MachineLearning #DeepLearning
ProAlign-DDI: A Zero-Shot Drug–Drug Interaction Event Prediction Framework with Prototype Alignment

ProAlign-DDI: A Zero-Shot Drug–Drug Interaction Event Prediction Framework with Prototype Alignment

Abstract. The accurate identification of drug–drug interaction (DDI) events (DDIEs) is essential for ensuring medication safety and preventing adverse effe

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