25 papers · ranked by Valyu relevance
Irini Doytchinova
Drug design is a complex pharmaceutical science with a long history. Many achievements have been made in the field of drug design since the end of 19th century, when Emil Fisher suggested that the drug-receptor interaction resembles the key and lock interplay. Gradually, drug design has been transformed into a coherent…
Jacqueline A. Sullivan, E. Richard Gold
Efforts by governments, firms, and patients to deliver pioneering drugs for critical health needs face a challenge of diminishing efficiency in developing those medicines. While multi-sectoral collaborations involving firms, researchers, patients, and policymakers are widely recognized as crucial for countering this…
Gaurab Aryal, Federico Ciliberto, Leland E. Farmer, Ekaterina Khmelnitskaya
'Ekaterina Khmelnitskaya'] We propose a methodology to estimate the market value of pharmaceutical drugs. Our approach combines an event study with a model of discounted cash flows and uses stock market responses to drug development announcements to infer the values. We estimate that, on average, a successful drug is…
Hezha O. Rasul, Dlzar D. Ghafour, Bakhtyar K. Aziz, Bryar A. Hassan + 2 more
'Tarik A. Rashid' 'Arif Kıvrak'] The drug development process is a critical challenge in the pharmaceutical industry due to its timeconsuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable…
Grazyna Biala, Ewa Kedzierska, Marta Kruk-Slomka, Jolanta Orzelska-Gorka + 7 more
'Jolanta Orzelska-Gorka' 'Sara Hmaidan' 'Aleksandra Skrok' 'Jakub Kaminski' 'Eva Havrankova' 'Dominika Nadaska' 'Ivan Malik' 'Ziyaur Rahman'] The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance, with many new and exciting techniques being developed…
Vefghi, Ali, Rahmati, Zahed + 2 more
Drug discovery remains a slow and expensive process that involves many steps, from detecting the target structure to obtaining approval from the Food and Drug Administration (FDA), and is often riddled with safety concerns. Accurate prediction of how drugs interact with their targets and the development of new drugs by…
Ágota Barabássy, Zsófia Borbála Dombi, György Németh, Felice Iasevoli
'Felice Iasevoli'] Until the late 1800s, drug development was a chance finding based on observations and repeated trials and errors. Today, drug development must go through many iterations and tests to ensure it is safe, potent, and effective. This process is a long and costly endeavor, with many pitfalls and hurdles.…
Srilekha Mamidala
– Drug repurposing is an emerging approach for drug discovery involving the reassignment of existing drugs for novel purposes. An alternative to the traditional de novo process of drug development, repurposed drugs are faster, cheaper, and less failure prone than drugs developed from traditional methods. Recently, drug…
Muhammad Usman, Sitaram Khadka, Mohammad Saleem, Huma Rasheed + 2 more
'Bimal Kunwar' 'Moshin Ali'] Pharmacotherapy, in many cases, is practiced at a suboptimal level of performance in low- and middle-income countries (LMICs) although stupendous amounts of data are available regularly. The process of drug development is time-consuming, costly, and is also associated with loads of hurdles…
Md. Aktar Hossain, Saima Sultana
In silico analysis is a powerful technique to identify better therapeutic interventions. Molecular docking is widely used to screen ligands through analysing binding affinities for target receptors. In this study we screened ligands for two proteins which are potential drug targets: deoxyuridine triphosphate…
Tianyang Wang, Ming Liu, Benji Peng, Xinyuan Song + 13 more
Development Authors: ['Tianyang Wang' 'Ming Liu' 'Benji Peng' 'Xinyuan Song' 'Charles Zhang' 'Xintian Sun' 'Qian Niu' 'Junyu Liu' 'Silin Chen' 'Keyu Chen' 'Ming Li' 'Pohsun Feng' 'Ziqian Bi' 'Yunze Wang' 'Yichao Zhang' 'Fei Cheng' 'Lingzhi Yan'] Tianyang Wanga , Ming Liub , Benji Peng, c, d, Xinyuan Songe , Charles…
Gaurav Sharma, Abhishek Thakur
ChatGPT is a language model developed by OpenAI. It is a machine learning model that has been trained on a large dataset of human language, allowing it to generate human-like text. It can be used for a variety of natural language processing tasks such as language translation, text summarization, and question answering.…
Michael Retchin, Yuanqing Wang, Kenichiro Takaba, John D. Chodera
Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization—predictive models and assays—are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before…
Authors not listed
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…
Md Rifat Hasan, Ahad Amer Alsaiari, Burhan Zain Fakhurji, Mohammad Habibur Rahman Molla + 9 more
The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long…
Daiki Erikawa, Nobuaki Yasuo, Masakazu Sekijima
Automatic optimization methods for compounds in the vast compound space are important for drug discovery and material design. Several machine learning-based molecular generative models for drug discovery have been proposed, but most of these methods generate compounds from scratch and are not suitable for exploring and…
Long Qian, Xin Lu, Parvez Haris, Jianyong Zhu + 2 more
Clinical trials are crucial for drug development, but they require significant time and financial resources. Additionally, uncertainties may arise during these trials concerning their results due to concerns surrounding effectiveness, safety, or the enrollment of participants. If robust AI (artificial intelligence)…
Chunyan Ao, Zhichao Xiao, Lixin Guan, Liang Yu
In recent decades, traditional drug research and development have been facing challenges such as high cost, long timelines, and high risks. To address these issues, many computational approaches have been suggested for predicting the relationship between drugs and diseases through drug repositioning, aiming to reduce…
Somnath Mondal, Debarghya Datta, Soumajit Pramanik, Rukmankesh Mehra
Finding drug-drug interaction is crucial for patient safety and treatment efficacy. Two drugs may show a synergistic effect but may sometimes cause a severe health issue, including lethality. Wet lab studies are often performed to understand such interactions but are limited by cost and time. However, the biochemical…
Anugraha Thyagatur, Nithin Sonti, Rahul Vijayan, Ayaan Parikh + 1 more
We present an AI-assisted pipeline for disease-specific drug landscape analysis. Given a disease name, the system assembles a comprehensive, evidence-based view of therapeutic assets by integrating structured sources (such as ClinicalTrials.gov and ChEMBL) and unstructured sources (such as publications, press releases…
Kyriaki Savva, Margarita Zachariou, Marilena Bourdakou, Nikolas Dietis + 1 more
In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as drugs from clinical trial studies in different phases. To our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards the…
Authors not listed
Several influential studies published around the turn of the millennium reported that advancing a lead compound to a drug tended to involve certain predictable changes in molecular properties, most notably marked increases in molecular weight (MW) and in lipophilicity. To assess how lead optimization practices have…
Authors not listed
Alzheimer’s disease (AD) is a neurodegenerative disorder associated with the accumulation of beta-amyloid plaques, oxidative stress, and a decrease in cholinergic activity among other pathologies. Given the limitations of current treatments, multitarget strategies present a promising alternative. This study prioritized…
Bo-Wei Zhao, Xiaorui Su, Peng-Wei Hu, Yu-Peng Ma + 2 more
The effectiveness of computational drug repositioning techniques has been further improved due to the development of artificial intelligence technology. However, most of the existing approaches fall short of taking into account the non-Euclidean nature of biomedical data. To overcome this problem, we propose a…
Austin Polanco, M. E. J. Newman
Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size…