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RetroRanker: leveraging reaction changes to improve retrosynthesis prediction through re-ranking
Retrosynthesis is an important task in organic chemistry. Recently, numerous data-driven approaches have achieved promising results in this task....
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Improving the performance of models for one-step retrosynthesis through re-ranking
AbstractRetrosynthesis is at the core of organic chemistry. Recently, the rapid growth of artificial intelligence (AI) has spurred a variety of novel...
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Artificial intelligence: machine learning for chemical sciences
Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial...
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AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application
We present an updated overview of the AiZynthFinder package for retrosynthesis planning. Since the first version was released in 2020, we have added...
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From theory to experiment: transformer-based generation enables rapid discovery of novel reactions
Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However,...
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AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry
AbstractThis paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction...
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Efficient retrosynthetic planning with MCTS exploration enhanced A* search
Retrosynthetic planning, which aims to identify synthetic pathways for target molecules from starting materials, is a fundamental problem in...
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DeepSA: a deep-learning driven predictor of compound synthesis accessibility
With the continuous development of artificial intelligence technology, more and more computational models for generating new molecules are being...
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AI for organic and polymer synthesis
Recent years have witnessed the transformative impact from the integration of artificial intelligence with organic and polymer synthesis. This...
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Autonomous design of new chemical reactions using a variational autoencoder
Artificial intelligence based chemistry models are a promising method of exploring chemical reaction design spaces. However, training datasets based...
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Synthetic organic chemistry driven by artificial intelligence
Synthetic organic chemistry underpins several areas of chemistry, including drug discovery, chemical biology, materials science and engineering....
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De novo design of bioactive phenol and chromone derivatives for inhibitors of Spike glycoprotein of SARS-CoV-2 in silico
This work presents the synthesis of 12 phenol and chromone derivatives, prepared by the analogs, and the possibility of conducting an in silico study...
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The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations
Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have...
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Integrating synthetic accessibility with AI-based generative drug design
Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not...
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Cell-Free Biosensors and AI Integration
Cell-free biosensors hold a great potential as alternatives for traditional analytical chemistry methods providing low-cost low-resource measurement...
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Global reactivity models are impactful in industrial synthesis applications
Artificial Intelligence is revolutionizing many aspects of the pharmaceutical industry. Deep learning models are now routinely applied to guide drug...
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The rise of self-driving labs in chemical and materials sciences
Accelerating the discovery of new molecules and materials, as well as develo** green and sustainable ways to synthesize them, will help to address...
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Retrosynthetic planning with experience-guided Monte Carlo tree search
In retrosynthetic planning, the huge number of possible routes to synthesize a complex molecule using simple building blocks leads to a combinatorial...
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A robotic platform for the synthesis of colloidal nanocrystals
Morphological control with broad tunability is a primary goal for the synthesis of colloidal nanocrystals with unique physicochemical properties....
