Journal ArticleDOI
Accelerating De Novo Drug Design against Novel Proteins Using Deep Learning.
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TLDR
In this article, the potential of deep learning and molecular modeling approaches was leveraged to develop a drug design pipeline, which can be useful for cases where there is limited or no availability of target-specific ligand datasets.Abstract:
In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug design. Yet, these methods face the problem of data scarcity while designing drugs against a novel target. In this work, the potential of deep learning and molecular modeling approaches was leveraged to develop a drug design pipeline, which can be useful for cases where there is limited or no availability of target-specific ligand datasets. Inhibitors of the homologues of the target protein were screened at the active site of the target protein to create an initial target-specific dataset. Transfer learning was used to learn the features of the target-specific dataset. A deep predictive model was utilized to predict the docking scores of newly designed molecules. Both these models were combined using reinforcement learning to design new chemical entities with an optimized docking score. The pipeline was validated by designing inhibitors against the human JAK2 protein, where none of the existing JAK2 inhibitors were used for training. The ability of the method to reproduce existing molecules from the validation dataset and design molecules with better binding energy demonstrates the potential of the proposed approach.read more
Citations
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Journal ArticleDOI
PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning.
Jannis Born,Jannis Born,Matteo Manica,Ali Oskooei,Joris Cadow,Greta Markert,Greta Markert,María Rodríguez Martínez +7 more
TL;DR: In this article, a hybrid VAE was used to generate drugs with high predicted efficacy against cell lines or cancer types, using an anticancer drug sensitivity prediction model as reward function.
Journal ArticleDOI
Application advances of deep learning methods for de novo drug design and molecular dynamics simulation
Qifeng Bai,Shuo Liu,Yanan Tian,Tingyang Xu,Antonio Jesús Banegas-Luna,Horacio Pérez-Sánchez,Junzhou Huang,Huanxiang Liu,Xiaojun Yao +8 more
TL;DR: Two major workflow methods and the related components of classical algorithm and deep learning are described for de novo drug design from a new perspective and IML is introduced for the deep learning model interpretability of de noVO drug design and MD simulations.
Journal ArticleDOI
LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design
Vendy Fialková,Jiaxi Zhao,Jiaxi Zhao,Kostas Papadopoulos,Ola Engkvist,Ola Engkvist,Esben Jannik Bjerrum,Thierry Kogej,Atanas Patronov +8 more
TL;DR: A novel tool for de novo drug design called LibINVENT, capable of rapidly proposing chemical libraries of compounds sharing the same core while maximizing a range of desirable properties, is presented.
Journal ArticleDOI
Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2
Jannis Born,Jannis Born,Matteo Manica,Joris Cadow,Greta Markert,Nil Adell Mill,Modestas Filipavicius,Nikita Janakarajan,Antonio Cardinale,Antonio Cardinale,Teodoro Laino,María Rodríguez Martínez +11 more
TL;DR: A deep learning framework for de novo discovery of molecules tailored to bind with given protein targets and the feasibility of swift chemical synthesis of molecules with potential antiviral properties that were designed against a specific protein target is demonstrated.
Journal ArticleDOI
De Novo Structure-Based Drug Design Using Deep Learning.
Sowmya Ramaswamy Krishnan,Navneet Bung,Sarveswara Rao Vangala,Rajgopal Srinivasan,Gopalakrishnan Bulusu,Arijit Roy +5 more
TL;DR: This work proposes a deep learning-based method, where the knowledge of the active site structure of the target protein is sufficient to design new molecules, and validated it against two well-studied proteins.
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