AIMC Topic: Chemistry, Pharmaceutical

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Digital Pharmaceutical Sciences.

AAPS PharmSciTech
Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, a...

Chemists: AI Is Here; Unite To Get the Benefits.

Journal of medicinal chemistry
The latest developments in artificial intelligence (AI) have arrived into an existing state of creative tension between computational and medicinal chemists. At their most productive, medicinal and computational chemists have made significant progres...

Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data.

Journal of chemical information and modeling
Contemporary deep learning approaches still struggle to bring a useful improvement in the field of drug discovery because of the challenges of sparse, noisy, and heterogeneous data that are typically encountered in this context. We use a state-of-the...

Medicinal Chemists versus Machines Challenge: What Will It Take to Adopt and Advance Artificial Intelligence for Drug Discovery?

Journal of chemical information and modeling
The field of artificial intelligence (AI) for generative chemistry is reaching the maturity stage, shifting the focus from the novelty of the algorithms to the quality of the generated molecules. To ensure continued evolution of AI technologies, we p...

The message passing neural networks for chemical property prediction on SMILES.

Methods (San Diego, Calif.)
Drug metabolism is determined by the biochemical and physiological properties of the drug molecule. To improve the performance of a drug property prediction model, it is important to extract complex molecular dynamics from limited data. Recent machin...

Transfer Learning: Making Retrosynthetic Predictions Based on a Small Chemical Reaction Dataset Scale to a New Level.

Molecules (Basel, Switzerland)
Effective computational prediction of complex or novel molecule syntheses can greatly help organic and medicinal chemistry. Retrosynthetic analysis is a method employed by chemists to predict synthetic routes to target compounds. The target compounds...

Learning Molecular Representations for Medicinal Chemistry.

Journal of medicinal chemistry
The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of informatics-driven research have relied on expert-designed molecular descriptors to establish qu...

Improvement in ADMET Prediction with Multitask Deep Featurization.

Journal of medicinal chemistry
The absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties of drug candidates are important for their efficacy and safety as therapeutics. Predicting ADMET properties has therefore been of great interest to the computation...

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.

Journal of medicinal chemistry
Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing synthetic planning into their overall appr...

An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with Recommendations.

Journal of medicinal chemistry
Artificial intelligence (AI) is becoming established in drug discovery. For example, many in the industry are applying machine learning approaches to target discovery or to optimize compound synthesis. While our organization is certainly applying the...