scholarly journals On the value of using 3D-shape and electrostatic similarities in deep generative methods

2021 ◽  
Author(s):  
Giovanni Bolcato ◽  
Jonas Boström
Keyword(s):  
State Of The Art ◽  
Molecular Shape ◽  
Scaffold Hopping ◽  
Improved Method ◽  
Generative Process ◽  
3D Shape ◽  
Partial Charges ◽  
Significant Extension ◽  
Improved Methods ◽  
Python Package

Multi-parameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compounds design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the generation of novel molecules with optimal properties. As before, the generative process outputs optimized molecules similar to the input structures, now with the improved feature of replacing parts of these molecules with fragments of similar 3D-shape and electrostatics. By performing comparisons of 3D-fragments, we can simulate 3D properties while overcoming the notoriously difficult step of accurately describing bioactive conformations. The comparison of electrostatic potential and molecular shape is performed using the new ESP-Sim python package, allowing the calculation of state-of-the-art partial charges (e.g., RESP with B3LYP/6-31G**) obtained using the quantum chemistry program Psi4. The new improved method is demonstrated with a scaffold-hopping exercise identifying CDK2 bioisosteres. All code is open-source and freely available.

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

scholarly journals Swarm-CG: Automatic Parametrization of Bonded Terms in Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

2020 ◽  
Author(s):  
Charly Empereur-mot ◽  
Luca Pesce ◽  
Davide Bochicchio ◽  
Claudio Perego ◽  
Giovanni M. Pavan
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Structural Complexity ◽  
Coarse Grained ◽  
Complex Molecules ◽  
Swarm Optimization ◽  
Molecular Systems ◽  
Self Tuning ◽  
User Friendly ◽  
Python Package

We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms within 4h-24h on standard desktop machines, using an AA trajectory as reference and default<br>settings of the software. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of molecular systems interesting for bio- and nanotechnology.<br>Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG force<br>fields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited for the optimization of models built based also on other CG schemes. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration data are available at: www.github.com/GMPavanLab/SwarmCG.

scholarly journals An Ab Initio Study of Li/Ni-doped NaxMeO2 Cathode Material for Na-Ion Batteries

2020 ◽  
Vol 03 (02) ◽  
pp. 1-1
Author(s):  
Arianna Massaro ◽  
◽  
Ana B. Muñoz-García ◽  
Mariarosaria Tuccillo ◽  
Michele Pavone ◽  
...  
Keyword(s):  
Cathode Material ◽  
State Of The Art ◽  
Magnetic Moments ◽  
Electronic Conduction ◽  
Random Structure ◽  
Cell Parameters ◽  
Adsorption Sites ◽  
Current State ◽  
Partial Charges ◽  
Na Ions

The current state-of-the-art quantum mechanics methodologies were applied to derive information on the bulk and surface properties of the P2-type layered oxide Na0.85Li0.17Ni0.21Mn0.64O2 (NLNMO), a cathode material. The special quasi-random structure (SQS) approach was employed to identify the arrangement of Li, Ni, and Mn ions in a supercell containing 115 atoms. Both the cell parameters and atomic positions were determined from DFT-PBE+U calculations to highlight specific distortions induced by the dopants (Ni and Li). The analysis of atomic partial charges and atomic magnetic moments revealed that Li has a purely structural role, while Ni and Mn actively participate in both redox processes and electronic conduction. Using a new surface slab model, the interaction between the layered Na0.85Li0.17Ni0.21Mn0.64O2 (001) surface and the Na ions was examined to identify the most favorable adsorption sites and the possible paths for the migration of the Na ions on the electrode surface.

scholarly journals MeshNet: Mesh Neural Network for 3D Shape Representation

2019 ◽  
Vol 33 ◽  
pp. 8279-8286 ◽  
Author(s):  
Yutong Feng ◽  
Yifan Feng ◽  
Haoxuan You ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Point Cloud ◽  
State Of The Art ◽  
Shape Representation ◽  
Shape Classification ◽  
Retrieval Performance ◽  
3D Shape ◽  
General Architecture ◽  
3D Shapes

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation.

scholarly journals MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval

2019 ◽  
Vol 33 ◽  
pp. 8513-8520 ◽  
Author(s):  
Jianwen Jiang ◽  
Di Bao ◽  
Ziqiang Chen ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Shape Representation ◽  
The State ◽  
Shape Retrieval ◽  
3D Shape Retrieval ◽  
3D Shape ◽  
Loop Level ◽  
Art Methods

3D shape retrieval has attracted much attention and become a hot topic in computer vision field recently.With the development of deep learning, 3D shape retrieval has also made great progress and many view-based methods have been introduced in recent years. However, how to represent 3D shapes better is still a challenging problem. At the same time, the intrinsic hierarchical associations among views still have not been well utilized. In order to tackle these problems, in this paper, we propose a multi-loop-view convolutional neural network (MLVCNN) framework for 3D shape retrieval. In this method, multiple groups of views are extracted from different loop directions first. Given these multiple loop views, the proposed MLVCNN framework introduces a hierarchical view-loop-shape architecture, i.e., the view level, the loop level, and the shape level, to conduct 3D shape representation from different scales. In the view-level, a convolutional neural network is first trained to extract view features. Then, the proposed Loop Normalization and LSTM are utilized for each loop of view to generate the loop-level features, which considering the intrinsic associations of the different views in the same loop. Finally, all the loop-level descriptors are combined into a shape-level descriptor for 3D shape representation, which is used for 3D shape retrieval. Our proposed method has been evaluated on the public 3D shape benchmark, i.e., ModelNet40. Experiments and comparisons with the state-of-the-art methods show that the proposed MLVCNN method can achieve significant performance improvement on 3D shape retrieval tasks. Our MLVCNN outperforms the state-of-the-art methods by the mAP of 4.84% in 3D shape retrieval task. We have also evaluated the performance of the proposed method on the 3D shape classification task where MLVCNN also achieves superior performance compared with recent methods.

scholarly journals It all starts with entities: A Salient entity topic model

2019 ◽  
Vol 26 (5) ◽  
pp. 531-549
Author(s):  
Chuan Wu ◽  
Evangelos Kanoulas ◽  
Maarten de Rijke
Keyword(s):  
Topic Model ◽  
State Of The Art ◽  
Topic Models ◽  
Generation Process ◽  
Qualitative And Quantitative Analysis ◽  
Generative Process ◽  
Qualitative And Quantitative ◽  
Proposed Model ◽  
Topic Distribution ◽  
Document Generation

AbstractEntities play an essential role in understanding textual documents, regardless of whether the documents are short, such as tweets, or long, such as news articles. In short textual documents, all entities mentioned are usually considered equally important because of the limited amount of information. In long textual documents, however, not all entities are equally important: some are salient and others are not. Traditional entity topic models (ETMs) focus on ways to incorporate entity information into topic models to better explain the generative process of documents. However, entities are usually treated equally, without considering whether they are salient or not. In this work, we propose a novel ETM, Salient Entity Topic Model, to take salient entities into consideration in the document generation process. In particular, we model salient entities as a source of topics used to generate words in documents, in addition to the topic distribution of documents used in traditional topic models. Qualitative and quantitative analysis is performed on the proposed model. Application to entity salience detection demonstrates the effectiveness of our model compared to the state-of-the-art topic model baselines.

scholarly journals DREAMTools: a Python package for scoring collaborative challenges

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1030 ◽  
Author(s):  
Thomas Cokelaer ◽  
Mukesh Bansal ◽  
Christopher Bare ◽  
Erhan Bilal ◽  
Brian M. Bot ◽  
...  
Keyword(s):  
Computational Methods ◽  
Training Data ◽  
Model Parameters ◽  
Automated Scoring ◽  
Statistical Machine Learning ◽  
Science And Engineering ◽  
New Methods ◽  
Wide Range ◽  
Improved Methods ◽  
Python Package

DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of September 2015, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform https://www.synapse.org.Availability: DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools.

scholarly journals DREAMTools: a Python package for scoring collaborative challenges

F1000Research ◽  
2016 ◽  
Vol 4 ◽  
pp. 1030 ◽  
Author(s):  
Thomas Cokelaer ◽  
Mukesh Bansal ◽  
Christopher Bare ◽  
Erhan Bilal ◽  
Brian M. Bot ◽  
...  
Keyword(s):  
Computational Methods ◽  
Training Data ◽  
Model Parameters ◽  
Automated Scoring ◽  
Statistical Machine Learning ◽  
Science And Engineering ◽  
Link Type ◽  
Wide Range ◽  
Improved Methods ◽  
Python Package

DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform at https://www.synapse.org.Availability: DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools/dreamtools.

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