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"Summary:A range of new and innovative tools used for preformulation and formulation of medicines help optimize pharmaceutical development projects. Such tools also assist with the performance evaluation of the pharmaceutical process, allowing any potential gaps to be identified. This book considers these key research and industrial tools."

"By focusing on general molecular mechanisms of antiviral drugs rather than therapies for individual viruses, this ready reference provides the critical knowledge needed to develop entirely novel therapeutics and to target new viruses.It begins with a general discussion of antiviral strategies, followed by a broad survey of known viral targets, such as reverse transcriptases, proteases, neuraminidases, RNA polymerases, helicases and primases, as well as their known inhibitors. The final section contains several cases studies of recent successful antiviral drug development.Edited by Erik de Clercq, the world authority on small molecule antiviral drugs, who has developed more new antivirals than anyone else."

"RNA Interference: Application to Drug Discovery and Challenges to Pharmaceutical Development provides a general overview of this rapidly emerging field, with a strong emphasis on issues and aspects that are important to a drug development team. The first part covers more general background of RNA interference and its application in drug discovery. In the second part, the book addresses siRNA (small interfering RNA), a pharmaceutically potent form, and its use and delivery in therapeutics along with manufacturing and delivery aspects."

"This volume covers all aspects of the antibiotic discovery and development process through Phase II/III. The contributors, a group of highly experienced individuals in both academics and industry, include chapters on the need for new antibiotic compounds, strategies for screening for new antibiotics, sources of novel synthetic and natural antibiotics, discovery phases of lead development and optimization, and candidate compound nominations into development. Beyond discovery , the handbook will cover all of the studies to prepare for IND submission : phase I (safety and dose ranging), progression to phase II (efficacy), and phase III (capturing desired initial indications). This book walks the reader through all aspects of the process, which has never been done before in a single reference. With the rise of antibiotic resistance and the increasing view that a crisis may be looming in infectious diseases, there are strong signs of renewed emphasis in antibiotic research. The purpose of the handbook is to offer a detailed overview of all aspects of the problem posed by antibiotic discovery and development."

"ABSTRACTVirtual screening (VS) is a computational technique used in drug discovery. Virtual Screening process usually works by identifying structures that are most likely to bind the target of drug. Virtual screening is usually based on compound similarity or database docking. Thus, the identification for drugcompounds based on structure classification still remain as a challenging task. The purpose of this research is to find a new approach for ligand-based virtual screening using machine learning technique. In this paper, theclassification has been done by using Deep Belief Networks (DBN) method. The data from Nicotinamide Adenine Dinucleotide (NAD) protein target family were used for training and testing the model. This research used four protein target classes from literature and two protein target classes from DUD-E docking website. Feature were obtained from molecular fingerprint descriptor. The experiments result show that DBN method outperform the existing pharmacophore approach."

"Kanker payudara menempati urutan kedua penyebab kematian wanita, pencegahannya dapat dilakukan dengan skrining dini dan meningkatkan kesadaran diri. Obat terapi hormon dengan target kadar estrogen menawarkan perawatan potensial. Namun, penemuan obat konvensional untuk perawatan kanker payudara memerlukan proses yang ekstensif dan mahal. Studi ini menyajikan kerangka kerja untuk menganalisis hubungan Quantitative Structure-Activity Relationship (QSAR) dari inhibitor reseptor estrogen  alfa. Pendekatan kami menggunakan supervised learning, mengintegrasikan informasi self-attention Transformer dan graf molekul untuk memprediksi inhibitor reseptor estrogen alfa. Kami melatih lima model klasifikasi untuk memprediksi inhibitor pada kanker payudara. Di antara semua model, model MATH yang kami usulkan mencapai precision, recall, f1-score, dan specifity yang unggul, dengan nilai masing-masing 0,952, 0,972, 0,960, dan 0,922, beserta dengan ROC-AUC 0,977. MATH menunjukkan kinerja yang kuat, menunjukkan potensi untuk membantu peneliti di bidang farmasi dan kesehatan khususnya dalam mengidentifikasi kandidat senyawa penghambat alfa estrogen dan memandu jalur penemuan obat.

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Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drugs that target estrogen levels offer potential treatments. However, conventional drug discovery entails extensive, costly processes. This study presents a framework for analyzing the quantitative structure-activity relationship (QSAR) of estrogen receptor alpha inhibitors. Our approach utilizes supervised learning, integrating self-attention Transformer and molecular graph information to predict estrogen receptor alpha inhibitors. We establish five classification models for predicting these inhibitors in breast cancer. Among these models, our proposed MATH model achieves remarkable precision, recall, f1-score, and specificity, with values of 0.952, 0.972, 0.960, and 0.922, respectively, alongside a ROC-AUC of 0.977. MATH exhibits robust performance, suggesting its potential to assist pharmaceutical and health researchers in identifying candidate compounds for estrogen alpha inhibitors and guiding drug discovery pathways."

"This book is the first systems biology text to focus on how systems biology can be specifically applied to enhance drug discovery and development, with particular emphasis on real-world examples. Other texts on systems biology to date have focused on particular subdisciplines of systems biology (such as cellular networks) and have not specifically addressed drug discovery and development. This book introduces key methodologies and technical approaches for helping to solve many of the current challenges facing the pharmaceutical and biotechnology industries."