7 citations · 10 across the 6 of their papers we have counts for
6 papers
Adapting Boltz-2 with limited experimental activity data improves early enrichment in virtual screening
Kairi Furui, Masahito Ohue
Virtual screening aims to prioritize active compounds from large chemical libraries within a limited experimental budget. When applying Boltz-2 to virtual screening, a key challeng…
Boltzina: Efficient and Accurate Virtual Screening via Docking-Guided Binding Prediction with Boltz-2
Kairi Furui, Masahito Ohue
In structure-based drug discovery, virtual screening using conventional molecular docking methods can be performed rapidly but suffers from limitations in prediction accuracy. Rece…
NPGPT: Natural Product-Like Compound Generation with GPT-based Chemical Language Models
Koh Sakano, Kairi Furui, Masahito Ohue
Natural products are substances produced by organisms in nature and often possess biological activity and structural diversity. Drug development based on natural products has been…
Active learning for energy-based antibody optimization and enhanced screening
Kairi Furui, Masahito Ohue
Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeutic antibody development. Although machine learning-based prediction methods a…
Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free Energy Perturbation
Kairi Furui, Masahito Ohue
In recent years, free energy perturbation (FEP) calculations have garnered increasing attention as tools to support drug discovery. The lead optimization mapper (Lomap) was propose…
Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain
Kairi Furui, Masahito Ohue
Learning-to-rank, a machine learning technique widely used in information retrieval, has recently been applied to the problem of ligand-based virtual screening, to accelerate the e…