AIMC Topic: Cheminformatics

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Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science.

Journal of biomolecular structure & dynamics
In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science a...

Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit.

Journal of chemical information and modeling
Advances in deep neural networks (DNNs) have made a very powerful machine learning method available to researchers across many fields of study, including the biomedical and cheminformatics communities, where DNNs help to improve tasks such as protein...

Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches.

Molecular diversity
Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several research...

Machine Learning Models to Predict Protein-Protein Interaction Inhibitors.

Molecules (Basel, Switzerland)
Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and ...

Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast.

Journal of chemical information and modeling
Deep learning has been a prevalence in computational chemistry and widely implemented in molecular property predictions. Recently, self-supervised learning (SSL), especially contrastive learning (CL), has gathered growing attention for the potential ...

Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery.

Journal of computer-aided molecular design
The support vector machine (SVM) algorithm is one of the most widely used machine learning (ML) methods for predicting active compounds and molecular properties. In chemoinformatics and drug discovery, SVM has been a state-of-the-art ML approach for ...

Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry.

International journal of molecular sciences
The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent succe...

Chemical Reactivity Prediction: Current Methods and Different Application Areas.

Molecular informatics
The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potentia...

Unsupervised Representation Learning for Proteochemometric Modeling.

International journal of molecular sciences
In silico protein-ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection a...

Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction.

Journal of chemical information and modeling
The estimation of chemical reaction properties such as activation energies, rates, or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural netw...