de novo design of bioactive small molecules by artificial intelligence (Merck KGaA)
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de novo design of bioactive small molecules by artificial intelligence
De Novo Design Of Bioactive Small Molecules By Artificial Intelligence, supplied by Merck KGaA, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/de+novo+design+of+bioactive+small+molecules+by+artificial+intelligence/de+novo+design+of+bioactive+small+molecules+by+artificial+intelligence/pm36065998-378-21-5
Average 90 stars, based on 1 article reviews
De Novo Design Of Bioactive Small Molecules By Artificial Intelligence, supplied by Merck KGaA, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/de+novo+design+of+bioactive+small+molecules+by+artificial+intelligence/de+novo+design+of+bioactive+small+molecules+by+artificial+intelligence/pm36065998-378-21-5
Average 90 stars, based on 1 article reviews
de novo design of bioactive small molecules by artificial intelligence - by Bioz Stars,
2026-09
90/100 stars
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other:Article Title: Autonomous Molecular Design: Then and Now. Article Snippet: The success of deep machine learning in processing of large amounts of data, for example, in image or voice recognition and generation, raises the possibilities that these tools can also be applied for solving complex problems in materials science.. In this forum article, we focus on molecular design that aims to answer the question on how we can predict and synthesize molecules with tailored physical, chemical, or biological properties.. A potential answer to this question could be found by using intelligent systems that integrate physical models and computational machine learning techniques with automated synthesis and characterization tools. Article Title: ChemistGA: A Chemical Synthesizable Accessible Molecular Generation Algorithm for Real-World Drug Discovery. Article Snippet: Many deep learning (DL)-based molecular generative models have been proposed to design novel molecules.. These models may perform well on benchmarks, but they usually do not take real-world constraints into account, such as available training data set, synthetic accessibility, and scaffold diversity in drug discovery.. In this study, a new algorithm, ChemistGA, was proposed by combining the traditional heuristic algorithm with DL, in which the crossover of the traditional genetic algorithm (GA) was redefined by DL in conjunction with GA, and an innovative backcrossing operation was implemented to generate desired molecules. Article Title: Scaffold-Constrained Molecular Generation. Article Snippet: Article Title: DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues. Article Snippet: In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of similar-to-bioactive compounds.. DeLA-Drug captures the syntax of SMILES strings of more than 1 million compounds belonging to the ChEMBL28 database and, by employing a new strategy called sampling with substitutions (SWS), generates molecules starting from a single user-defined query compound.. Remarkably, the algorithm preserves druglikeness and synthetic accessibility of the known bioactive compounds present in the ChEMBL28 repository. Article Title: Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds. Article Snippet: Multi-targeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation or the metabolic syndrome.. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin.. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Article Title: Can Generative-Model-Based Drug Design Become a New Normal in Drug Discovery? Article Snippet: It is still rare that AI application examples with full DMTA (Design, Make, Test, Analysis) outcomes are reported.. A recent study highlights that a generative model could be applied in the drug discovery process through an example in which ideas generated by an AI generative model were confirmed by following wet-lab works.. D discovery is a notoriously lengthy and costly process which is often described as finding a needle in a haystack. |