437 research outputs found
MIANN models in medicinal, physical and organic chemistry
[Abstract] Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational techniques that can be used in this sense. In any case, almost all these methods focus on few fundamental aspects including: type (1) methods to quantify the molecular structure, type (2) methods to link the structure with the biological activity, and others. In particular, MARCH-INSIDE (MI), acronym for Markov Chain Invariants for Networks Simulation and Design, is a well-known method for QSAR analysis useful in step (1). In addition, the bio-inspired Artificial-Intelligence (AI) algorithms called Artificial Neural Networks (ANNs) are among the most powerful type (2) methods. We can combine MI with ANNs in order to seek QSAR models, a strategy which is called herein MIANN (MI & ANN models). One of the first applications of the MIANN strategy was in the development of new QSAR models for drug discovery. MIANN strategy has been expanded to the QSAR study of proteins, protein-drug interactions, and protein-protein interaction networks. In this paper, we review for the first time many interesting aspects of the MIANN strategy including theoretical basis, implementation in web servers, and examples of applications in Medicinal and Biological chemistry. We also report new applications of the MIANN strategy in Medicinal chemistry and the first examples in Physical and Organic Chemistry, as well. In so doing, we developed new MIANN models for several self-assembly physicochemical properties of surfactants and large reaction networks in organic synthesis. In some of the new examples we also present experimental results which were not published up to date.Ministerio de Ciencia e Innovación; CTQ2009-07733Universidad del Pais Vasco; UFI11/22Universidad del Pais Vasco; GIU 094
Operationally meaningful representations of physical systems in neural networks
To make progress in science, we often build abstract representations of
physical systems that meaningfully encode information about the systems. The
representations learnt by most current machine learning techniques reflect
statistical structure present in the training data; however, these methods do
not allow us to specify explicit and operationally meaningful requirements on
the representation. Here, we present a neural network architecture based on the
notion that agents dealing with different aspects of a physical system should
be able to communicate relevant information as efficiently as possible to one
another. This produces representations that separate different parameters which
are useful for making statements about the physical system in different
experimental settings. We present examples involving both classical and quantum
physics. For instance, our architecture finds a compact representation of an
arbitrary two-qubit system that separates local parameters from parameters
describing quantum correlations. We further show that this method can be
combined with reinforcement learning to enable representation learning within
interactive scenarios where agents need to explore experimental settings to
identify relevant variables.Comment: 24 pages, 13 figure
ResMGCN: Residual Message Graph Convolution Network for Fast Biomedical Interactions Discovering
Biomedical information graphs are crucial for interaction discovering of
biomedical information in modern age, such as identification of multifarious
molecular interactions and drug discovery, which attracts increasing interests
in biomedicine, bioinformatics, and human healthcare communities. Nowadays,
more and more graph neural networks have been proposed to learn the entities of
biomedical information and precisely reveal biomedical molecule interactions
with state-of-the-art results. These methods remedy the fading of features from
a far distance but suffer from remedying such problem at the expensive cost of
redundant memory and time. In our paper, we propose a novel Residual Message
Graph Convolution Network (ResMGCN) for fast and precise biomedical interaction
prediction in a different idea. Specifically, instead of enhancing the message
from far nodes, ResMGCN aggregates lower-order information with the next round
higher information to guide the node update to obtain a more meaningful node
representation. ResMGCN is able to perceive and preserve various messages from
the previous layer and high-order information in the current layer with least
memory and time cost to obtain informative representations of biomedical
entities. We conduct experiments on four biomedical interaction network
datasets, including protein-protein, drug-drug, drug-target, and gene-disease
interactions, which demonstrates that ResMGCN outperforms previous
state-of-the-art models while achieving superb effectiveness on both storage
and time.Comment: In progres
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