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A scikit-learn-compatible, lightweight Python package for machine learning with rejections.
YAMLChem is a Python package built on top of MXNet and RDKit for applying machine learning methods in computational chemistry.
Published in Intelligent systems. Theory and applications, 2018
An upper bound for the chromatic number of graphs depending on thickness and girth.
Recommended citation: S. Sh. Adilov, “An upper bound for the chromatic number of graphs with given thickness and girth”, Intelligent systems. Theory and applications, 22:3 (2018), 99–104 http://mi.mathnet.ru/eng/ista/v22/i3/p99
Published in ChemRxiv, 2021
A comprehensive outline of SMILES-based autoregressive language models.
Recommended citation: Adilov, Sanjar (2021): Neural Language Modeling for Molecule Generation. ChemRxiv. Preprint. doi:10.26434/chemrxiv.14700831.v1 https://doi.org/10.26434/chemrxiv.14700831.v1
Published in ChemRxiv, 2021
Pre-training a multi-task GPT-2 transformer for drug design. Transfer learning with adapter modules for molecular-property prediction and focused molecule generation.
Recommended citation: Adilov, Sanjar (2021): Generative Pre-Training from Molecules. ChemRxiv. Preprint. doi:10.33774/chemrxiv-2021-5fwjd http://doi.org/10.33774/chemrxiv-2021-5fwjd
Published in ChemRxiv, 2022
Penalized linear models enforcing sparsity on grouped molecular representations.
Recommended citation: Adilov, Sanjar (2022): Molecular-Property Prediction with Sparsity. ChemRxiv. Preprint. doi:10.26434/chemrxiv-2022-g7mfn http://doi.org/10.26434/chemrxiv-2022-g7mfn
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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