EDITORIAL: Improving Neuropharmacology using Big Data, Machine Learning and Computational Algorithms

Institute of Next Generation Healthcare (INGH), Icahn Institute of Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Mount Sinai Health System, Manhattan, NY USA; Bioinformatics Research Laboratory, Eminent Biosciences, Vijaynagar, Indore-452010, Madhya Pradesh, India; In silico Research Laboratory, Legene Biosciences, Vijaynagar, Indore-452010, Madhya Pradesh, India; IMQ Zorrotzaurre Clinic, Ballets Olaeta Kalea, 4, 48014 Bilbao, Bizkaia Spain; Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Biscay, Spain; IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Biscay, Spain

[1]  S. Muresan,et al.  Chemical predictive modelling to improve compound quality , 2013, Nature Reviews Drug Discovery.

[2]  Jürgen Bajorath,et al.  Integration of virtual and high-throughput screening , 2002, Nature Reviews Drug Discovery.

[3]  Joel Dudley,et al.  Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment , 2016, Briefings Bioinform..

[4]  J. Aronson,et al.  From basic to clinical neuropharmacology: targetophilia or pharmacodynamics? , 2012, British journal of clinical pharmacology.

[5]  Eric Vilain,et al.  Clinical exome sequencing for genetic identification of rare Mendelian disorders. , 2014, JAMA.

[6]  Christopher G Chute,et al.  Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol. , 2014, Mayo Clinic proceedings.

[7]  Riccardo Miotto,et al.  Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams , 2016, Briefings Bioinform..

[8]  R. DeBiasi,et al.  West Nile virus neuroinvasive disease , 2006, Annals of neurology.

[9]  M. Yadav,et al.  Development of MLR and SVM Aided QSAR Models to Identify Common SAR of GABA Uptake Herbal Inhibitors used in the Treatment of Schizophrenia , 2017, Current neuropharmacology.

[10]  L. Hou,et al.  Effect of Methylphenidate in Patients with Cancer-Related Fatigue: A Systematic Review and Meta-Analysis , 2014, PloS one.

[11]  Khader Shameer,et al.  In silico methods for drug repurposing and pharmacology , 2016, Wiley interdisciplinary reviews. Systems biology and medicine.

[12]  Aliuska Morales Helguera,et al.  Chemoinformatics Profiling of the Chromone Nucleus as a MAO-B/A2AAR Dual Binding Scaffold , 2017, Current neuropharmacology.

[13]  S. Singh,et al.  Advantages of Structure-Based Drug Design Approaches in Neurological Disorders , 2017, Current neuropharmacology.

[14]  Xuan Yuan,et al.  Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders , 2014, Science Translational Medicine.

[15]  M. Parise,et al.  Treatment of malaria in the United States: a systematic review. , 2007, JAMA.

[16]  G. Keserű,et al.  Integration of virtual and high throughput screening in lead discovery settings. , 2011, Combinatorial chemistry & high throughput screening.

[17]  M. Natália D. S. Cordeiro,et al.  Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson’s Disease Dual Target Ligands , 2017, Current neuropharmacology.

[18]  Ponnuthurai N. Suganthan,et al.  Bioinformatics and Biology Insights , 2022 .

[19]  J. Thirthalli,et al.  A Comparative Study of Short Term Efficacy of Aripiprazole and Risperidone in Schizophrenia , 2017, Current neuropharmacology.

[20]  Khader Shameer,et al.  Functional repertoire, molecular pathways and diseases associated with 3D domain swapping in the human proteome , 2012, Journal of Clinical Bioinformatics.

[21]  K. Pollard,et al.  Genomic and network patterns of schizophrenia genetic variation in human evolutionary accelerated regions. , 2015, Molecular biology and evolution.

[22]  Michael J. Keiser,et al.  Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach. , 2016, The lancet. Psychiatry.

[23]  Khader Shameer,et al.  3DSwap: curated knowledgebase of proteins involved in 3D domain swapping , 2011, Database J. Biol. Databases Curation.

[24]  R. Sowdhamini,et al.  Comparative Genomics of Odorant Binding Proteins in Anopheles gambiae, Aedes aegypti, and Culex quinquefasciatus , 2013, Genome biology and evolution.

[25]  C. Haslam,et al.  Neuropsychological and psychiatric profiles in acute encephalitis in adults , 2007, Neuropsychological rehabilitation.

[26]  R Brookmeyer,et al.  Projections of Alzheimer's disease in the United States and the public health impact of delaying disease onset. , 1998, American journal of public health.

[27]  Christopher G. Chute,et al.  A Template for Authoring and Adapting Genomic Medicine Content in the eMERGE Infobutton Project , 2014, AMIA.

[28]  J. Dickens,et al.  Olfactory disruption: toward controlling important insect vectors of disease. , 2015, Progress in molecular biology and translational science.

[29]  Z. Walker,et al.  Are cholinesterase inhibitors effective in the management of the behavioral and psychological symptoms of dementia in Alzheimer's disease? A systematic review of randomized, placebo-controlled trials of donepezil, rivastigmine and galantamine , 2009, International Psychogeriatrics.

[30]  Ramanathan Sowdhamini,et al.  3dswap-pred: prediction of 3D domain swapping from protein sequence using Random Forest approach. , 2011, Protein and peptide letters.

[31]  Jessica D. Tenenbaum,et al.  Translational Bioinformatics: Past, Present, and Future , 2016, Genom. Proteom. Bioinform..

[32]  Neuropharmacology of the injured spinal cord , 1987, Paraplegia.

[33]  Ramanathan Sowdhamini,et al.  Structural Analysis of Prolyl Oligopeptidases Using Molecular Docking and Dynamics: Insights into Conformational Changes and Ligand Binding , 2011, PloS one.

[34]  Khader Shameer,et al.  Computational and experimental advances in drug repositioning for accelerated therapeutic stratification. , 2015, Current topics in medicinal chemistry.

[35]  R. Sowdhamini,et al.  Molecular modeling and docking studies of human 5-hydroxytryptamine 2A (5-HT2A) receptor for the identification of hotspots for ligand binding. , 2009, Molecular bioSystems.

[36]  Rajeev K. Singla,et al.  In Silico Studies Revealed Multiple Neurological Targets for the Antidepressant Molecule Ursolic Acid. , 2017, Current neuropharmacology.

[37]  M. Yadav,et al.  Common SAR Derived from Linear and Non-linear QSAR Studies on AChE Inhibitors used in the Treatment of Alzheimer’s Disease , 2017, Current neuropharmacology.

[38]  Doru Georg Margineanu,et al.  Neuropharmacology beyond reductionism - A likely prospect , 2016, Biosyst..

[39]  Ramanathan Sowdhamini,et al.  DOCKSCORE: a webserver for ranking protein-protein docked poses , 2015, BMC Bioinformatics.

[40]  Joshua L. Deignan,et al.  Exome sequencing in the clinical diagnosis of sporadic or familial cerebellar ataxia. , 2014, JAMA neurology.