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Cheminformatics

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Augmented and Programmatically Optimized LLM Prompts Reduce Chemical Hallucinations.

Journal of chemical information and modeling
Utilizing large Language models (LLMs) for handling scientific information comes with risk of the outputs not matching expectations, commonly called hallucinations. To fully utilize LLMs in research requires improving their accuracy, avoiding halluci...

Deciphering Molecular Embeddings with Centered Kernel Alignment.

Journal of chemical information and modeling
Analyzing machine learning models, especially nonlinear ones, poses significant challenges. In this context, centered kernel alignment (CKA) has emerged as a promising model analysis tool that assesses the similarity between two embeddings. CKA's eff...

Research Progresses and Applications of Knowledge Graph Embedding Technique in Chemistry.

Journal of chemical information and modeling
A knowledge graph (KG) is a technique for modeling entities and their interrelations. Knowledge graph embedding (KGE) translates these entities and relationships into a continuous vector space to facilitate dense and efficient representations. In the...

MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn.

Journal of chemical information and modeling
The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the ...

Molecular tweaking by generative cheminformatics and ligand-protein structures for rational drug discovery.

Bioorganic chemistry
The purpose of this review is two-fold: (1) to summarize artificial intelligence and machine learning approaches and document the role of ligand-protein structures in directing drug discovery; (2) to present examples of drugs from the recent literatu...

Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.

Molecular informatics
This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characterist...

[Development of drug discovery support system using chemoinformatics and generative AI technology].

Nihon yakurigaku zasshi. Folia pharmacologica Japonica
In recent years, the rapid development of generative AI has given rise to a variety of services such as machine translation, sentence summarization, and programming code generation. In drug discovery, generative AI and chemoinformatics have been used...

Identification of CXCR4 inhibitory activity in natural compounds using cheminformatics-guided machine learning algorithms.

Integrative biology : quantitative biosciences from nano to macro
Neurodegenerative disorders are characterised by progressive damage to neurons that leads to cognitive impairment and motor dysfunction. Current treatment options focus only on symptom management and palliative care, without addressing their root cau...

Automated Workflows for Data Curation and Machine Learning to Develop Quantitative Structure-Activity Relationships.

Methods in molecular biology (Clifton, N.J.)
The recent advancements in machine learning and the new availability of large chemical datasets made the development of tools and protocols for computational chemistry a topic of high interest. In this chapter a standard procedure to develop Quantita...

HiRXN: Hierarchical Attention-Based Representation Learning for Chemical Reaction.

Journal of chemical information and modeling
In recent years, natural language processing (NLP) techniques, including large language modeling (LLM), have contributed significantly to advancements in organic chemistry research. Chemical reaction representations provide a link between NLP models ...