Saeed Moayedpour
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View article: mRNA-LM: full-length integrated SLM for mRNA analysis
mRNA-LM: full-length integrated SLM for mRNA analysis Open
The success of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) messenger RNA (mRNA) vaccine has led to increased interest in the design and use of mRNA for vaccines and therapeutics. Still, selecting the most appropriate mRNA …
View article: Many-Shot In-Context Learning for Molecular Inverse Design
Many-Shot In-Context Learning for Molecular Inverse Design Open
Large Language Models (LLMs) have demonstrated great performance in few-shot In-Context Learning (ICL) for a variety of generative and discriminative chemical design tasks. The newly expanded context windows of LLMs can further improve ICL…
View article: CodonBERT large language model for mRNA vaccines
CodonBERT large language model for mRNA vaccines Open
mRNA-based vaccines and therapeutics are gaining popularity and usage across a wide range of conditions. One of the critical issues when designing such mRNAs is sequence optimization. Even small proteins or peptides can be encoded by an en…
View article: Representations of lipid nanoparticles using large language models for transfection efficiency prediction
Representations of lipid nanoparticles using large language models for transfection efficiency prediction Open
Motivation Lipid nanoparticles (LNPs) are the most widely used vehicles for mRNA vaccine delivery. The structure of the lipids composing the LNPs can have a major impact on the effectiveness of the mRNA payload. Several properties should b…
View article: Deep Batch Active Learning for Drug Discovery
Deep Batch Active Learning for Drug Discovery Open
A key challenge in drug discovery is to optimize, in silico, various absorption and affinity properties of small molecules. One strategy that was proposed for such optimization process is active learning. In active learning molecules are s…
View article: Author Response: Deep Batch Active Learning for Drug Discovery
Author Response: Deep Batch Active Learning for Drug Discovery Open
A key challenge in drug discovery is to optimize, in silico, various absorption and affinity properties of small molecules. One strategy that was proposed for such optimization process is active learning. In active learning molecules are s…
View article: Author Response: Deep Batch Active Learning for Drug Discovery
Author Response: Deep Batch Active Learning for Drug Discovery Open
A key challenge in drug discovery is to optimize, in silico, various absorption and affinity properties of small molecules. One strategy that was proposed for such optimization process is active learning. In active learning molecules are s…
View article: Deep Batch Active Learning for Drug Discovery
Deep Batch Active Learning for Drug Discovery Open
A key challenge in drug discovery is to optimize, in silico, various absorption and affinity properties of small molecules. One strategy that was proposed for such optimization process is active learning. In active learning molecules are s…
View article: Deep Batch Active Learning for Drug Discovery
Deep Batch Active Learning for Drug Discovery Open
A key challenge in drug discovery is to optimize, in silico, various absorption and affinity properties of small molecules. One strategy that was proposed for such optimization process is active learning. In active learning molecules are s…
View article: CodonBERT: Large Language Models for mRNA design and optimization
CodonBERT: Large Language Models for mRNA design and optimization Open
A bstract mRNA based vaccines and therapeutics are gaining popularity and usage across a wide range of conditions. One of the critical issues when designing such mRNAs is sequence optimization. Even small proteins or peptides can be encode…
View article: Deep Batch Active Learning for Drug Discovery
Deep Batch Active Learning for Drug Discovery Open
A key challenge in drug discovery is to optimize, in silico, various absorption and affinity properties of small molecules. One strategy that was proposed for such optimization process is active learning. In active learning molecules are s…
View article: Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ) on Tetrathiafulvalene (TTF)
Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ) on Tetrathiafulvalene (TTF) Open
Highly ordered epitaxial interfaces between organic semiconductors are considered as a promising avenue for enhancing the performance of organic electronic devices including solar cells and transistors, thanks to their well-controlled, uni…
View article: Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for TCNQ on TTF
Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for TCNQ on TTF Open
Highly ordered epitaxial interfaces between organic semiconductors are considered as a promising avenue for enhancing the performance of organic electronic devices including solar cells, light emitting diodes, and transistors, thanks to th…
View article: First Principles Study of the Electronic Structure of the Ni$_2$MnIn/InAs and Ti$_2$MnIn/InSb interfaces
First Principles Study of the Electronic Structure of the Ni$_2$MnIn/InAs and Ti$_2$MnIn/InSb interfaces Open
We present a first-principles study of the electronic and magnetic properties of epitaxial interfaces between the Heusler compounds Ti$_2$MnIn and Ni$_2$MnIn and the III-V semiconductors, InSb and InAs, respectively. We use density functio…
View article: Structure prediction of epitaxial inorganic interfaces by lattice and surface matching with Ogre
Structure prediction of epitaxial inorganic interfaces by lattice and surface matching with Ogre Open
We present a new version of the Ogre open source Python package with the capability to perform structure prediction of epitaxial inorganic interfaces by lattice and surface matching. In the lattice matching step, a scan over combinations o…
View article: Dependence of the electronic structure of the EuS/InAs interface on the bonding configuration
Dependence of the electronic structure of the EuS/InAs interface on the bonding configuration Open
Recently, the EuS/InAs interface has attracted attention for the possibility\nof inducing magnetic exchange correlations in a strong spin-orbit\nsemiconductor, which could be useful for topological quantum devices. We use\ndensity function…