Gargoyles: An Open Source Graph-based molecular optimization method based on Deep Reinforcement Learning [PDF]
ACS Omega, 2023 Automatic optimization methods for compounds in the vast compound space are important for drug discovery and material design. Several machine learning-based molecular generative models for drug discovery have been proposed, but most of these methods ...
Nobuaki, Yasuo +2 more
core +2 more sources
Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates [PDF]
Journal of CheminformaticsMolecular optimization is a crucial step in drug development, involving structural modifications to improve the desired properties of drug candidates. Although many deep-learning-based molecular optimization algorithms have been proposed and may perform ...
Xiaodan Yin +12 more
doaj +2 more sources
Mol-CycleGAN: a generative model for molecular optimization [PDF]
Journal of Cheminformatics, 2020 Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties.
Łukasz Maziarka +5 more
doaj +2 more sources
Sculpting molecules in text-3D space: a flexible substructure aware framework for text-oriented molecular optimization [PDF]
BMC BioinformaticsThe integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming the landscape of scientific research.
Kaiwei Zhang +7 more
doaj +2 more sources
Physics-informed latent-space optimization for energy-aligned hole-collecting monolayers in inverted perovskite solar cells [PDF]
RSC AdvancesPrecise energy-level alignment at buried interfaces is critical for high-performance inverted perovskite solar cells, where the highest occupied molecular orbital (HOMO) of hole-collecting monolayers (HCMs) must be tuned relative to the valence band ...
Naomu Sekiguchi, Satoshi Iikubo
doaj +2 more sources
Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning [PDF]
Communications ChemistryDesigning molecules with desirable properties is a critical endeavor in drug discovery. Because of recent advances in deep learning, molecular generative models have been developed.
Shogo Nakamura +2 more
doaj +2 more sources
EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties [PDF]
Journal of CheminformaticsDesigning molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three ...
Xiaobo Lin +5 more
doaj +2 more sources
Optimization of General Molecular Properties in the Equilibrium Geometry Using Quantum Alchemy: An Inverse Molecular Design Approach [PDF]
, 2023 Inverse molecular design allows optimization of molecules in chemical space and is promising for accelerating the development of functional molecules and materials.
Takafumi, Shiraogawa, Jun-ya, Hasegawa
core +2 more sources
Molecular Optimization by Capturing Chemist’s Intuition Using Deep Neural Networks [PDF]
, 2020 A main challenge in drug discovery is finding molecules with a desirable balance of multiple properties. Here, we focus on the task of molecular optimization, where the goal is to optimize a given starting molecule towards desirable properties. This task
eva, nittinger +7 more
core +2 more sources
Replication Data for: eSLP optimization algorithm
, 2023 m-files implementing the eSLP optimization algorithm for the three simulations (3.1. - 3.3.) described in the associated paper. File README.txt contains analytical directions regarding requirements, how to verify the results and videos generated by the ...
Optimization, eSLP
core +1 more source