Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
Magnetovariational studies have been carried out in Singhbhum and surrounding regions during 1987 and 1989. Three deep-seated linear conductors have been identified. One of them is located to the north of Ranchi, Bokaro and Purulia extending in E-W direction coinciding with high heat flow region and Gondwana sediments. The trend of anomaly at Ranchi and Purulia at longer periods suggests a conductivity anomaly due to the mafic and ultramafic intrusions, considered to be responsible for the uplift of Chhotanagpur plateau. The second conductor is associated with the basin margin fault that separates the Singhbhum craton and Chhotanagpur plateau from the West Bengal basin. This conductive zone appears to extend further south and join the high heat flow region of Attri-Tarabalo. This conductor could be isolated only after eliminating the coast effect from the observed induction vectors. The third conductive zone follows the trend of Mahanadi valley located south of the Sukinda thrust. Conductive anomaly associated with the Sukinda and Singhbhum thrust zones could not be resolved due to the interference from neighbouring conductive structures. These two thrusts may not be very deep-seated structures. The Singhbhum granite batholith is found to be highly resistive and seems to extend to greater depths.
Data Structure Through C In Depth S K Srivastava
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The course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to build and encapsulate data objects and their associated operations. Specific topics include linked structures, recursive structures and algorithms, binary trees, balanced trees, and hash tables. There will be weekly assignments, consisting of programming and written exercises, a midterm, and a final exam. Prerequisites: CMPSCI 121 (or equivalent Java experience) and Basic Math Skills (R1). Basic Java language concepts are introduced quickly; if unsure of background, contact instructor. 4 credits.
Introduction to database management. Relational database topics include data modeling, query languages, database design, and query optimization. Alternative data management approaches will be covered, including semi-structured data, XML, and text retrieval. Application topics will include web data management, integration of data sources, security and privacy. Prerequisite: CMPSCI 220 (or 287) and CMPSCI 311. 3 credits.
The structure of digital computers is studied at several levels, from the basic logic level, to the component level, to the system level. Topics include: the design of basic components such as arithmetic units and registers from logic gates; the organization of basic subsystems such as the memory and I/O subsystems; the interplay between hardware and software in a computer system; the von Neumann architecture and its performance enhancements such as cache memory, instruction and data pipelines, coprocessors, and parallelism. Weekly assignments, semester project, 2 hours exams, final. Prerequisites: CMPSCI 201 and CMPSCI 220 (or CMPSCI 287). 3 credits.
Crowdsourcing, or the act of outsourcing a task to the crowd, has the potential to revolutionize information collection and processing systems by enabling in-depth, large-scale, and cost-effective information gathering, more accurate techniques for information extraction from data. Crowdsourced data processing is effective when humans are better than existing automated computer algorithms, for example labeling images, transcribing speech, annotating text, transcribing scanned documents, and so on. Crowdsourcing also provides a powerful mechanism for creating data about the physical world, particularly through the use of mobile phones and their rich set of on-board sensors (GPS, audio, video, accelerometer, etc). These sensors can be utilized to provide continuous and unprecedented visibility into the state of the world across many scales. This course is an exploration of the opportunities and challenges of crowdsourcing, and will discuss a variety of perspectives on the topic including applications, incentives, data quality assurance, privacy, general design principles, incorporation of ML/AI techniques, modeling, integration with social networks and cloud computing, etc. Students from diverse research backgrounds/interests are encouraged to attend for more productive discussion. Lect A=3 credits; Lect B=1 credit.
This 1-credit Honors course is designed to combine community service learning with a mechanism for reinforcing learning through tutoring. The Honors students will tutor students at Holyoke Community College (HCC) enrolled in a one-hour-per-week, seven-week Web design class, IT is all about me. The HCC curriculum is similar to the web programming component of CMPSCI 120, but with much less depth and a focus on developmental students. The Honors students will work with minority and/or non-native-English speakers in a course for students with weak college preparation, but with the intent to introduce students to the Web and to encourage students to move on to IT majors. Tutoring will occur at 2-3 in-person visits to HCC and via phone, email, text messaging, etc. It is expected that tutoring HCC students will reinforce learning of CMPSCI 120 content. Each student will attend a two-day training session at the Learning Resource Center and take a refresher course every month. Students will prepare a self-reflexive paper that discusses the tutoring experience, comments, outcomes and suggestions about the experience and improvements. As a prerequisite, students must be currently enrolled in or have previously taken CMPSCI 120. Students fluent in Spanish or Russian particularly are encouraged to enroll.
The honors section of CMPSCI 535 provides an opportunity for University Honors students enrolled in the class to take a deeper look at some aspect of computer architecture or its underlying technology. The specific choice of topics is agreed upon by the instructor and student on an individual basis. Students may choose to explore the history of some aspect of architecture or technology, look at market influences on the science and engineering of computer hardware, experiment with a novel computer design through simulation, conduct a series of in-depth readings leading to a semester thesis, or other suitable work done under regular consultation with the instructor. Recommended for Juniors, Seniors; Majors. 1 credit. 589ccfa754
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