Compound 47
A medicinal chemist and her team push a promising drug candidate from simulation to synthesis.
41 genre.works
A medicinal chemist and her team push a promising drug candidate from simulation to synthesis.
The techniques and challenges of similarity search to find promising candidates from vast compound libraries.
Understanding the limitations and possibilities of docking calculation scoring functions through real examples.
A dialogue exploring drug repositioning strategies to discover new uses for existing drugs.
Quantifying compound-protein interaction patterns to gain new perspectives on activity prediction.
Exploring DNA promoter sequence optimization that controls gene expression from the fusion of machine learning and biology.
A dialogue exploring the possibilities and challenges of protein design using machine learning.
A conversation exploring how small structural changes affect drug activity from the perspective of compound-based drug design.
Exploring strategies to improve translation efficiency through mRNA sequence optimization from a machine learning perspective.
A dialogue about analyzing protein binding pocket structures and aiming for optimal compound design.
The difficulty of docking that accounts for protein structural changes, and strategies to overcome it.
A story exploring molecular similarity search and cheminformatics practice.
A story exploring protein flexibility and the induced-fit phenomenon.
A story exploring gene expression control and promoter sequence design.
A story exploring protein binding pocket structure analysis and molecular recognition.
A story exploring the effects of substituents in structure optimization and structure-activity relationships.
A story exploring protein expression efficiency improvement through mRNA sequence optimization.
A story exploring understanding of binding modes through interaction fingerprint analysis.
A story exploring finding new indications for existing drugs and unexpected pharmacological activities.
A story exploring computational design and structure prediction of functional proteins.
A story exploring the limitations and possibilities of docking scoring functions.
Learning metabolic stability and metabolic site prediction, understanding how compounds change in the body.
Learning molecular design strategies for how to utilize space within binding pockets.
Learning molecular design strategies to increase target selectivity and reduce side effects.
Learning how machine learning and AI contribute to drug design, their possibilities and limitations.
Learning the process of finding molecules that satisfy multiple constraints through multi-parameter optimization.
Learning the decisive impact that just one hydrogen bond can have on activity.
Understanding how electronic effects influence molecular properties and activity through substituent electronic characteristics.
Learning how small substituent differences affect activity from a structure-activity relationship perspective.
Understanding the impact of lipophilicity and hydrophilicity balance on pharmacokinetics.
Learning the basic principles of drug-likeness through Lipinski's Rule of Five.
Understanding the three-dimensional structure of binding sites reveals the direction of molecular design.
Understanding the impact of a single hydrogen bond on binding affinity and the importance of its optimization.
Understanding activity differences in structurally similar compounds and cultivating the ability to identify important structural features.
The strength of hydrophobic interactions and strategies for molecular design that maximize their use.
Understanding what computational chemistry scoring functions evaluate and what they overlook.
Exploring how conformational isomers and their energy differences affect binding affinity and selectivity.
Understanding how small structural changes affect activity from the perspective of structure-activity relationships.
Exploring how electron density distribution affects molecular reactivity and binding ability.
The mechanism by which subtle balances of steric hindrance and electronic effects trigger dramatic activity changes.
Understanding the utility and limitations of quantitative structure-activity relationship models and learning their appropriate use.