AIMC Topic: Cheminformatics

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Representation of Molecules by Sequences of Instructions.

Journal of chemical information and modeling
The processing of chemical information by computational intelligence methods faces the challenge of the structural complexity of molecular graphs. These graphs are not amenable to being represented in a suitable way for such methods. The most popular...

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...

SimSon: simple contrastive learning of SMILES for molecular property prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Molecular property prediction with deep learning has accelerated drug discovery and retrosynthesis. However, the shortage of labeled molecular data and the challenge of generalizing across the vast chemical spaces pose significant hurdles...

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...

[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...

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...

Development of Drug Discovery Platforms Using Artificial Intelligence and Cheminformatics.

Chemical & pharmaceutical bulletin
Recently, remarkable progress has been achieved in artificial intelligence (AI), including machine learning. Various AI models have been proposed for drug discovery, including the design of small molecules, activity prediction, and three-dimensional ...

Exploring Anti-osteoporosis Medicinal Herbs using Cheminformatics and Deep Learning Approaches.

Combinatorial chemistry & high throughput screening
BACKGROUND: Osteoporosis is a prevalent disease for the aged population. Chinese herbderived natural compounds have anti-osteoporosis effects. Due to the complexity of chemical ingredients and natural products, it is necessary to develop a high-throu...