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Sampling From Molecular Unnormalized Distributions With Deep Generative Models Toward the Acceleration of Molecular Design and Conformational Sampling With Deep Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Sampling From Molecular Unnormalized Distributions With Deep Generative Models Toward the Acceleration of Molecular Design and Conformational Sampling With Deep Learning./
Author:	Viguera Diez, Juan.
Description:	1 online resource (39 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798382229409

Sampling From Molecular Unnormalized Distributions With Deep Generative Models Toward the Acceleration of Molecular Design and Conformational Sampling With Deep Learning.
Viguera Diez, Juan.

Sampling From Molecular Unnormalized Distributions With Deep Generative Models Toward the Acceleration of Molecular Design and Conformational Sampling With Deep Learning. - 1 online resource (39 pages)

Source: Masters Abstracts International, Volume: 85-11.

Thesis (Licentiate)--Chalmers Tekniska Hogskola (Sweden), 2024.

Includes bibliographical references

This thesis investigates how Deep Generative Models (DGMs) can address important drug discovery problems involving sampling from unnormalized distributions. It consists of two papers focusing on this challenge's aspects: molecular design and conformational sampling. The first paper proposes a new training scheme to fine-tune graph-based DGMs for de novo molecular design. Our method can produce molecules with specific properties even when they are scarce or missing in the training data and outperforms previously reported graph-based methods on predicted dopamine receptor type D2 activity while maintaining diversity. The second paper develops Surrogate Model-Assisted Molecular Dynamics (SMA-MD), which combines a DGM with statistical reweighting and short Molecular Dynamics simulations to generate equilibrium ensembles of molecules. SMA-MD can produce more diverse and lower energy ensembles than conventional molecular dynamics simulations. These contributions constitute important stepping stones towards the automation of the drug discovery process.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382229409Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

CheminformaticsIndex Terms--Genre/Form:

554714
Electronic books.

Sampling From Molecular Unnormalized Distributions With Deep Generative Models Toward the Acceleration of Molecular Design and Conformational Sampling With Deep Learning.
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