Development of Scoring-Assisted Generative Exploration (SAGE) and Its Application to Enzyme Inhibitor Design | Chapter 11 | Pharmaceutical Research: Recent Advances and Trends Vol. 5

 In this study, an effective computational methodology named Scoring-Assisted Generative Exploration (SAGE) was devised by integrating the GEGL framework and multiple QSAR models. De novo molecular design, which involves exploring chemical space for drug-like molecules with desired properties, has been significantly advanced by deep learning techniques. Generative deep learning has revolutionized the field of de novo molecular design by enabling direct learning from input data without relying on human-made rules. This study introduces an innovative computational method, Scoring-Assisted Generative Exploration (SAGE), designed to enhance chemical diversity and optimize properties through virtual synthesis simulation, generation of bridged bicyclic rings, and application of multiple scoring models for drug-likeness. SAGE was tested on six protein targets and successfully generated high-scoring molecules within reasonable steps by optimizing for target specificity, synthetic accessibility, solubility, and metabolic stability. Additionally, SAGE identified a top-ranked molecule as a dual inhibitor of acetylcholinesterase and monoamine oxidase B, demonstrating its capability to optimize multiple properties simultaneously. These findings underscore the potential of SAGE and de novo design strategies in advancing drug discovery and development. With the ability to rapidly explore vast chemical spaces and generate novel molecules with desired properties, deep learning-based approaches like SAGE have the potential to revolutionize the field of drug discovery and development.

 

Author(s) Details

Hocheol Lim
Bioinformatics and Molecular Design Research Center (BMDRC), Incheon, Republic of Korea.

 

Please see the book here:- https://doi.org/10.9734/bpi/prrat/v5/1928

In this study, an effective computational methodology named Scoring-Assisted Generative Exploration (SAGE) was devised by integrating the GEGL framework and multiple QSAR models. De novo molecular design, which involves exploring chemical space for drug-like molecules with desired properties, has been significantly advanced by deep learning techniques. Generative deep learning has revolutionized the field of de novo molecular design by enabling direct learning from input data without relying on human-made rules. This study introduces an innovative computational method, Scoring-Assisted Generative Exploration (SAGE), designed to enhance chemical diversity and optimize properties through virtual synthesis simulation, generation of bridged bicyclic rings, and application of multiple scoring models for drug-likeness. SAGE was tested on six protein targets and successfully generated high-scoring molecules within reasonable steps by optimizing for target specificity, synthetic accessibility, solubility, and metabolic stability. Additionally, SAGE identified a top-ranked molecule as a dual inhibitor of acetylcholinesterase and monoamine oxidase B, demonstrating its capability to optimize multiple properties simultaneously. These findings underscore the potential of SAGE and de novo design strategies in advancing drug discovery and development. With the ability to rapidly explore vast chemical spaces and generate novel molecules with desired properties, deep learning-based approaches like SAGE have the potential to revolutionize the field of drug discovery and development.

 

Author(s) Details

Hocheol Lim
Bioinformatics and Molecular Design Research Center (BMDRC), Incheon, Republic of Korea.

 

Please see the book here:- https://doi.org/10.9734/bpi/prrat/v5/1928

Drug Discovery in Pharmaceutical Chemistry: An In-Depth Exploration | Chapter 8 | Pharmaceutical Research - Recent Advances and Trends Vol. 3

Drug discovery in pharmaceutical chemistry is a complex, multi-stage process aimed at identifying new compounds that can become effective therapeutic agents. This process involves several key phases: target identification and validation, hit identification, hit-to-lead development, lead optimization, preclinical development, and clinical development, culminating in regulatory approval. Each stage integrates diverse scientific disciplines and advanced technologies to ensure the discovery of viable drug candidates. Challenges such as high failure rates, complex disease biology, safety and toxicity concerns, and significant time and cost investments are inherent to this process. However, emerging trends like artificial intelligence, machine learning, personalized medicine, and the development of biologics and biosimilars are revolutionizing drug discovery, enhancing efficiency, and paving the way for more targeted and effective treatments. This exploration provides a detailed overview of each stage, highlights the associated challenges, and discusses the innovative trends that are shaping the future of pharmaceutical research.

 

Author (s) Details

Shivkant Patel
Department of Pharmacy, Sumandeep Vidyapeeth Deemed to be University, At and Po. Piparia, Ta. Waghodia, 391760, Vadodara, Gujarat, India.

 

Dillip Kumar Dash
Department of Pharmacy, Sumandeep Vidyapeeth Deemed to be University, At and Po. Piparia, Ta. Waghodia, 391760, Vadodara, Gujarat, India.

 

Dipti Gohil
Department of Pharmacy, Sumandeep Vidyapeeth Deemed to be University, At and Po. Piparia, Ta. Waghodia, 391760, Vadodara, Gujarat, India.

Ashim Kumar Sen
Department of Pharmacy, Sumandeep Vidyapeeth Deemed to be University, At and Po. Piparia, Ta. Waghodia, 391760, Vadodara, Gujarat, India.

 

Dhanya B. Sen
Department of Pharmacy, Sumandeep Vidyapeeth Deemed to be University, At and Po. Piparia, Ta. Waghodia, 391760, Vadodara, Gujarat, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/prrat/v3/981

Bioinformatics-driven Approaches in Modern Drug Design and Development | Chapter 7 | Innovations in Science and Technology: Shaping a Sustainable Future (Vol. 1)

Bioinformatics serves as the comprehensive solution for addressing various challenges in drug design, encompassing issues such as high costs, staffing needs, technical expertise requirements, regulatory constraints, and time limitations. Despite significant investments in financial and human resources, drug development endeavours often fall short of achieving market success. The genomic sequence generated by bioinformatics is pivotal, expediting gene identification for scientists. Traditional drug development processes are not only costly and time-consuming but also prone to failure. Industry observers emphasize the importance of streamlining drug development and discovery to uphold pharmaceutical companies & profitability and productivity. Numerous fields are included in bioinformatics, such as population genetics, transcriptomics, proteomics, genomic analysis, and molecular phylogenetics. Within the process of drug discovery, the persons working in the field of bioinformatics leverage molecular data having high throughput to compare symptomatic subjects against normal controls, aiming to establish connections between disease symptoms and various genetic, epigenetic, and environmental factors. Key objectives include identifying drug targets, refining drug candidates, and assessing potential drug resistance and environmental impacts. As a data-driven field, bioinformatics continually evolves, adapting databases and algorithms to novel data types. Its applications are vital for predicting biomolecules supporting treatment, prevention, and mechanisms of action against infectious diseases, utilizing tools to analyze biological data from diverse omics fields. Moreover, bioinformatics methodologies facilitate experimental molecular biology by extracting meaningful insights from extensive raw data, particularly in genetics, where it aids in genome sequencing, annotation, and visualization of mutations. Additionally, bioinformatics enables the storage, management, and prediction of drug targets, laying the groundwork for further research to solidify links between targets and diseases. Overall, bioinformatics empowers computers to genetic interactions, and handle pathways, structures, sequences and functions, shaping the forefront of biological research and drug discovery.

 

Author(s)details:-

 

Ripu Daman
Department of Biotechnology, Chaudhary Bansi Lal University, Bhiwani, Haryana, India.

 

Ripu Daman
Department of Biotechnology, Chaudhary Bansi Lal University, Bhiwani, Haryana, India.

 

Please See the book here :- https://doi.org/10.9734/bpi/mono/978-81-973809-6-9/CH7

Heterocyclic Compounds Containing Bifunctional β -Aminoketone Skeleton | Chapter 2 | Recent Developments in Chemistry and Biochemistry Research Vol. 1

The β -aminoketone fragment is seldom found in several drugs, natural products, and bioactive heterocyclic compounds. The development of novel techniques for preparing β -aminoketones using new technologies has been the subject of numerous recent studies. The aim is to produce products with better yields, faster reaction rates, and lower costs. The main focus of this book chapter is to provide the reader with a broad overview on the synthesis of a variety of heterocycles possessing  β -aminoketone skeleton such as piperidines, pyridopyrazinones, quinolizinones, morpholinones, Indoloisoquinolines, isoindolines, pyrazoles, polycyclic quinolinones, pyrrolidinones, imidazolines, indolizines, indolines, benzodiazepines and many others. Several of these heterocycles show encouraging biological activities that could be crucial in small molecule drug discovery efforts.

Author(s) Details:

Karan Singh,
Department of Chemistry, Indira Gandhi University, Meerpur, Rewari-122502, Haryana, India.

Please see the link here: https://stm.bookpi.org/RDCBR-V1/article/view/13609

Drug Repurposing of Pharmaceutical Products and Antimicrobial Discovery | Chapter 2 | Advanced Concepts in Pharmaceutical Research Vol. 7

The process of identifying new therapeutic uses for existing medications is known as drug repurposing. It's a good way to find or develop new drug compounds with different pharmacological applications. In recent years, numerous pharmaceutical companies have used the drug reformulation technique in their Research and Development of pharmaceuticals programmes to produce new medications based on the identification of new biological drug targets. This technique is extremely efficient, saves time, minimal cost, and has a low risk of failure. It boosts a drug's therapeutic value and its success rate. As a result, drug repositioning is a viable alternative to the standard drug development procedure. Identifying novel molecular compounds by de novo approach of drug is challenging, tedious and costly endeavor. To identify novel use of drug molecules many laboratory and drug interactions is been done. It is thus thought to be a developing method in existing drug, which have previously been shown safe in people and are in turn used to tackle rare and complicated diseases. Biopharmaceutical businesses have struggled to get the anticipated results when aiming to boost productivity through novel discovery technologies. Repositioning existing medications for new indications could help the industry achieve productivity gains. More firms are looking for repositioning options in their existing pharmacopoeia, and the number of drug repurposing has many success stories.

Author(s) Details:

Haripriya G.,
Department of Pharmacognosy, JSS College of Pharmacy, Mysuru, JSS Academy of Higher Education and Research, Mysuru- 570015, Karnataka, India.

Jatin Batra,
Department of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education, Mysuru-570015, Karnataka, India.

Alan Joseph,
Department of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education, Mysuru-570015, Karnataka, India.

Please see the link here: https://stm.bookpi.org/ACPR-V7/article/view/13540

Artificial Intelligence (AI) and Its Application in Pharmacy | Chapter 12 | Advances and Challenges in Science and Technology Vol. 9

 The AI is an active tool for data excavating based on the gigantic pharmacological data and machine learning process. Therefore, AI has been used in again drug design, activity scoring, in essence screening and in silico judgment in the properties (absorption, classification, metabolism, excretion and toxicity) of a drug particle. AI has made important contributions to the healthcare industry in any of areas, including the administration and storage of data and news about patient medical histories, cure stocks, sale records, and more; automated appliance; software and computer uses; and diagnostic finishes like CT and MRI diagnostics. All of these have been grown to support and streamline healthcare procedures. As expected, artificial intelligence (AI) has transformed healthcare expected more effective and adept, and the pharmaceutical industry is not exempt. All along the past few years, a substantial amount of increasing interest in the uses of AI science has been identified for resolving as well as interpreting few important fields of pharmacy like drug finding, dosage form crafty, poly pharmacology, and hospital pharmacy. AI-located solutions have been labeled which involve platforms that can make use of a difference of data types viz. manifestations reported for one patients, biometrics, imaging, biomarkers, etc. We engaged to produce a thorough report that would aid every undertaking pharmacist in understanding the major progresses made likely by the application of machine intelligence (AI), in light of the field's expanding meaning. In order to conclude the outbreaks of COVID-19, Zika, Ebola, and seasonal influenza, deep education and neural networks were utilized. Accompanying the advancement of AI technologies, the experimental community concede possibility witness rapid and cost-effective healthcare and drug research as well as provide upgraded service to the society.

Author(s) Details:

R. Radha,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

V. Neelima,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

M. P. VedaVarshan,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

S. Saqib Basha,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

U. Jeevitha,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

M. A. KapilKumar,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

G. Sateesh Kumar,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

Please see the link here: https://stm.bookpi.org/ACST-V9/article/view/12634

From Code to Cure: The Role of AI in Accelerating Drug Discovery | Chapter 6 | Advances and Challenges in Science and Technology Vol. 2

 Technology is detracting in every facet of life, containing drug discovery. The healthcare area is heavily dependent on drug finding to combat an increasing predominance of diseases in societies, with new cures being required to address drug fighting. Artificial intelligence (AI) is becoming more influential due to raised drug discovery complicatedness as more factors must be considered before a drug is brought in. Development of medicines immediately requires concern of several determinants that affect buyers and the developers, making it owned by ensure effectiveness to achieve a balance betwixt development and sustainability. Drug discoveries immediately use cutting-edge algorithms and vehicle-learning approaches to embellish processes and achieve settled goals like drug influence and security. To increase the use of machine intelligence as one of the sciences being implemented, the study implies a methodology that integrates AI into a drug discovery process. The plan was established to embellish data study and drug prediction through reliance on algorithms that optimize the process. The paper contains a block diagram to show the differing parts of the process and a flowchart that shows the launch of AI in the simulated process.

Author(s) Details:

Alex Mathew,
Department of Cybersecurity & Data Science, Bethany College, USA.

Hannah Alex,
Dietrich School of Science, University of Pittsburgh, Pennsylvania, USA.

Please see the link here: https://stm.bookpi.org/ACST-V2/article/view/11929

Exploring the Impact of Artificial Intelligence and Machine Learning in New Drug Discovery | Chapter 6 | Research Highlights in Science and Technology Vol. 7

 Artificial intelligence (AI) is a meaningful and emerging district of research. A comprehensive reasoning of the information included in the dossier requires the use of AI electronics, which are immediately more important than before at the capacity and rate of data unification. In order to advance research and enhance decision-making across a off-course range of professions and disciplines, containing drug design, upgrading, expression development, pharmacology, pharmacokinetics, microscopic and cell plant structure and toxicity, AI is required for drug finding and development. AI is crucial for reinforcing growing patient society selection, patient stratum, and patient sample evaluation in dispassionate trials biomarkers, productiveness metrics, dose collection, and study duration). Increasing significance of AI in pharmaceutical finding and development, in addition to the rising number of foundation businesses that specialized situated on sides. Estimations show that the process of developing a new drug is questioning, expensive, and has a depressed success rate: An average of $1.3 billion is gone on R&D for each cure. Oncology drugs take an average of 13.1 years to plan, while non-oncology drugs take between 5.9 and 7.2 years. 13.8% of all drug-happening projects complete with approval. The drug growth business is convinced AI/ML techniques by way of their automated type, predictive abilities, and an wonted rise in efficiency in this place review focus on part of Artificial intelligence in drug discovery and happening process.

Author(s) Details:

T. Sundarrajan,

Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India.

D. Priya,

Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India.

M. K. Kathiravan,

Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India.

V. Velmurugan,

Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India.

G. V. Anjana,

Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India.

B. Shanthakumar,

Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India.

R. Srimathi,

Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India.

Please see the link here: https://stm.bookpi.org/RHST-V7/article/view/11400

Multidisciplinary Approaches in Pharmaceutical Sciences| Book Publisher International

Multidisciplinary approaches in pharmaceutical sciences have gained significant recognition and importance in recent years. This approach involves integrating knowledge and expertise from various fields such as chemistry, biology, pharmacology, engineering, and computer science to address complex challenges in drug discovery, development, and delivery. By combining different disciplines, researchers can explore new avenues for drug design, identify novel therapeutic targets, and develop innovative drug delivery systems. For example, computational modeling and bioinformatics techniques enable the prediction of drug-target interactions and optimization of drug structures, leading to more efficient and targeted therapies. Furthermore, nanotechnology and biomaterials offer promising solutions for enhancing drug stability, solubility, and controlled release.Multidisciplinary collaboration also facilitates a comprehensive understanding of drug action and safety, integrating pharmacokinetics, pharmacodynamics, toxicology, and clinical research. By considering diverse perspectives, pharmaceutical scientists can develop safer and more effective drugs, tailored to individual patients or specific disease conditions. Overall, multidisciplinary approaches in pharmaceutical sciences foster innovation and accelerate advancements in the field, ultimately improving patient outcomes and revolutionizing the pharmaceutical industry.

Author(s) Details:

Ghanshyam Parmar,
Department of Pharmacy, Sumandeep Vidyapeeth Deemed to be University, Piparia, Vadodara-391760, Gujarat, India.

Ashish P. Shah,
Department of Pharmacy, Sumandeep Vidyapeeth Deemed to be University, Piparia, Vadodara-391760, Gujarat, India.

Please see the link here: https://stm.bookpi.org/MAPS/article/view/10941

A Short Update on Cheminformatics for Prompting the Process of Drug Design and Discovery | Chapter 4 | Challenges and Advances in Chemical Science Vol. 2

 Cheminformatics is one of the newest topics in science, bringing cutting-edge state-of-the-art knowledge to the table. This paper provides an updated report on new publications in Cheminformatics and related topics that may be useful throughout the drug design and discovery process, as well as the development of therapeutic medications.

Author (S) Details

Daniel Glossman-Mitnik
Laboratorio Virtual NANOCOSMOS, Departamento de Medio Ambiente y Energ´?a, Centro de Investigacion en Materiales Avanzados, Chihuahua, Chih 31136, Mexico.

View Book :- https://stm.bookpi.org/CACS-V2/article/view/2653

Recent Advances on Natural Product Inspired Design and Synthesis of Medicinally Active Heterocycles | Book Publisher International

 Mother nature is a key source of treatment for practically all diseases, as well as inspiration for synthetic and medicinal chemists to explore new chemical spaces. Natural molecules have proven to play an important function in medicine and chemotherapy. These natural chemicals have gradually encouraged synthetic chemists to change natural product molecules in desired ways in order to incorporate medicinal potential. Natural product inspired synthesis is the result of this trend of modifying natural molecules in desirable ways. The proven importance of natural molecules and natural product inspired synthesis prompted us to write this book, which includes a comprehensive list of natural product derived molecules, extensive synthetic methodologies, and biological potential against various targets. The tiny synthetic compounds have been narrowed down and classified based on their biological activity and targets. Anticancer, antidiabetic, antifungal, antibacterial, antitubercular, antileishmanial, and other activities were included in the study. The pathway for derivatization of various pharmacophoric nuclei selected from bioactive natural compounds has been investigated. The molecule's detailed synthesis methodology is presented in an easy-to-understand format, allowing the structure and activity of the molecule to be linked to specific targets.

Author(s) Detailts

Vishwa Deepak Tripathi
Department of Chemistry, M. K. College Laheriasarai, Lalit Narayan Mithila University, Darbhanga, Bihar, India.

View Book:- https://stm.bookpi.org/RANPIDSMAH/article/view/1892