[HTML][HTML] GNINA 1.0: molecular docking with deep learning
Molecular docking computationally predicts the conformation of a small molecule when
binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline …
binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline …
[HTML][HTML] Structure-based discovery of small molecules that disaggregate Alzheimer's disease tissue derived tau fibrils in vitro
Alzheimer's disease (AD) is the consequence of neuronal death and brain atrophy
associated with the aggregation of protein tau into fibrils. Thus disaggregation of tau fibrils …
associated with the aggregation of protein tau into fibrils. Thus disaggregation of tau fibrils …
A 3D generative model for structure-based drug design
We study a fundamental problem in structure-based drug design---generating molecules
that bind to specific protein binding sites. While we have witnessed the great success of …
that bind to specific protein binding sites. While we have witnessed the great success of …
[HTML][HTML] Artificial intelligence for drug discovery: Resources, methods, and applications
W Chen, X Liu, S Zhang, S Chen - Molecular Therapy-Nucleic Acids, 2023 - cell.com
Conventional wet laboratory testing, validations, and synthetic procedures are costly and
time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques …
time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques …
[HTML][HTML] TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations
TURBOMOLE is a collaborative, multi-national software development project aiming to
provide highly efficient and stable computational tools for quantum chemical simulations of …
provide highly efficient and stable computational tools for quantum chemical simulations of …
Computational discovery of transition-metal complexes: from high-throughput screening to machine learning
Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
[HTML][HTML] Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model
The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly
spreading, and the incidence rate is increasing worldwide. Due to the lack of effective …
spreading, and the incidence rate is increasing worldwide. Due to the lack of effective …
[HTML][HTML] Inverse design of 3d molecular structures with conditional generative neural networks
The rational design of molecules with desired properties is a long-standing challenge in
chemistry. Generative neural networks have emerged as a powerful approach to sample …
chemistry. Generative neural networks have emerged as a powerful approach to sample …
[HTML][HTML] BIOPEP-UWM database of bioactive peptides: Current opportunities
P Minkiewicz, A Iwaniak, M Darewicz - International journal of molecular …, 2019 - mdpi.com
The BIOPEP-UWM™ database of bioactive peptides (formerly BIOPEP) has recently
become a popular tool in the research on bioactive peptides, especially on these derived …
become a popular tool in the research on bioactive peptides, especially on these derived …
SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules
A Daina, O Michielin, V Zoete - Nucleic acids research, 2019 - academic.oup.com
SwissTargetPrediction is a web tool, on-line since 2014, that aims to predict the most
probable protein targets of small molecules. Predictions are based on the similarity principle …
probable protein targets of small molecules. Predictions are based on the similarity principle …