A review of large language models and autonomous agents in chemistry
Large language models (LLMs) are emerging as a powerful tool in chemistry across multiple
domains. In chemistry, LLMs are able to accurately predict properties, design new …
domains. In chemistry, LLMs are able to accurately predict properties, design new …
Sample efficient reinforcement learning with active learning for molecular design
Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions
in high-dimensional action spaces. However, bridging the gap between playing computer …
in high-dimensional action spaces. However, bridging the gap between playing computer …
Exhaustive local chemical space exploration using a transformer model
How many near-neighbors does a molecule have? This fundamental question in chemistry
is crucial for molecular optimization problems under the similarity principle assumption …
is crucial for molecular optimization problems under the similarity principle assumption …
The recent advances in the approach of artificial intelligence (AI) towards drug discovery
Artificial intelligence (AI) has recently emerged as a unique developmental influence that is
playing an important role in the development of medicine. The AI medium is showing the …
playing an important role in the development of medicine. The AI medium is showing the …
De novo generated combinatorial library design
Artificial intelligence (AI) contributes new methods for designing compounds in drug
discovery, ranging from de novo design models suggesting new molecular structures or …
discovery, ranging from de novo design models suggesting new molecular structures or …
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation
Many computational chemistry and molecular simulation workflows can be expressed as
graphs. This abstraction is useful to modularize and potentially reuse existing components …
graphs. This abstraction is useful to modularize and potentially reuse existing components …
Drug discovery: In silico dry data can bypass biological wet data?
M Tognolini, A Lodola, C Giorgio - British Journal of …, 2024 - Wiley Online Library
The recent and extraordinary increase in computer power, along with the availability of
efficient algorithms based on artificial intelligence, has prompted a large number of …
efficient algorithms based on artificial intelligence, has prompted a large number of …
Machine learning-guided strategies for reaction conditions design and optimization
This review surveys the recent advances and challenges in predicting and optimizing
reaction conditions using machine learning techniques. The paper emphasizes the …
reaction conditions using machine learning techniques. The paper emphasizes the …
Integrated Framework of Fragment-Based Method and Generative Model for Lead Drug Molecules Discovery
U Chude Okonkwo, O Lehasa - Available at SSRN 4801900 - papers.ssrn.com
Generative models have proven valuable in generating novel lead molecules with drug-like
properties. However, beyond generating drug-like molecules, the generative model should …
properties. However, beyond generating drug-like molecules, the generative model should …