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pro vyhledávání: '"Kim, So Youn"'
Understanding transition paths between meta-stable states in molecular systems is fundamental for material design and drug discovery. However, sampling these paths via unbiased molecular dynamics simulations is computationally prohibitive due to the
Externí odkaz:
http://arxiv.org/abs/2405.19961
Advancements in deep generative modeling have changed the paradigm of drug discovery. Among such approaches, target-aware methods that exploit 3D structures of protein pockets were spotlighted for generating ligand molecules with their plausible bind
Externí odkaz:
http://arxiv.org/abs/2405.16861
Optimizing molecules to improve their properties is a fundamental challenge in drug design. For a fine-tuning of molecular properties without losing bio-activity validated in advance, the concept of bioisosterism has emerged. Many in silico methods h
Externí odkaz:
http://arxiv.org/abs/2403.02706
Autor:
Shen, Tony, Seo, Seonghwan, Lee, Grayson, Pandey, Mohit, Smith, Jason R, Cherkasov, Artem, Kim, Woo Youn, Ester, Martin
Publikováno v:
NeurIPS 2023 Generative AI and Biology (GenBio) Workshop
Searching the vast chemical space for drug-like and synthesizable molecules with high binding affinity to a protein pocket is a challenging task in drug discovery. Recently, molecular deep generative models have been introduced which promise to be mo
Externí odkaz:
http://arxiv.org/abs/2310.03223
Autor:
Seo, Seonghwan, Kim, Woo Youn
Publikováno v:
NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development
As the size of accessible compound libraries expands to over 10 billion, the need for more efficient structure-based virtual screening methods is emerging. Different pre-screening methods have been developed for rapid screening, but there is still a
Externí odkaz:
http://arxiv.org/abs/2310.00681
Understanding the interactions of a solute with its environment is of fundamental importance in chemistry and biology. In this work, we propose a deep neural network architecture for atom type embeddings in its molecular context and interatomic poten
Externí odkaz:
http://arxiv.org/abs/2309.15334
The choice of boundary condition makes an essential difference in the solution structure of diffusion equations. The Dirichlet and Neumann boundary conditions and their combination have been the most used, but their legitimacy has been disputed. We s
Externí odkaz:
http://arxiv.org/abs/2308.00416
We study the appearance of a boundary condition along an interface between two regions, one with constant diffusivity $1$ and the other with diffusivity $\eps>0$, when $\eps\to0$. In particular, we take Fick's diffusion law in a context of reaction-d
Externí odkaz:
http://arxiv.org/abs/2308.00321
Prediction of protein-ligand interactions (PLI) plays a crucial role in drug discovery as it guides the identification and optimization of molecules that effectively bind to target proteins. Despite remarkable advances in deep learning-based PLI pred
Externí odkaz:
http://arxiv.org/abs/2307.01066
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However
Externí odkaz:
http://arxiv.org/abs/2304.12233