26 papers · ranked by Valyu relevance
Péter Csermely, Tamás Korcsmáros, Huba Kiss, Gábor London + 1 more
'Ruth Nussinov'] Abstract: Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level…
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…
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…
Zheng Yao Low, Isra Ahmad Farouk, Sunil Kumar Lal
Traditionally, drug discovery utilises a de novo design approach, which requires high cost and many years of drug development before it reaches the market. Novel drug development does not always account for orphan diseases, which have low demand and hence low-profit margins for drug developers. Recently, drug…
E.L. Andrade, A.F. Bento, J. Cavalli, S.K. Oliveira + 5 more
in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies Authors: ['E.L. Andrade' 'A.F. Bento' 'J. Cavalli' 'S.K. Oliveira' 'C.S. Freitas' 'R. Marcon' 'R.C. Schwanke' 'J.M. Siqueira' 'J.B. Calixto'] This review presents a historical overview of drug…
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…
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…
Munveer Thind, Peter R. Kowey
The Food and Drug Administration (FDA) is responsible for the regulation of the pharmaceutical industry in the interest of protecting public health. The aim of this review was to outline the evolution and current role of the FDA in the development and approval of new drugs. Additionally, we describe current assessments…
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.…
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…
Aroon D. Hingorani, Valerie Kuan, Chris Finan, Felix A. Kruger + 9 more
Drug development depends on accurately identifying molecular targets that both play a causal role in a disease and are amenable to pharmacological action by small molecule drugs or bio-therapeutics, such as monoclonal antibodies. Errors in drug target specification contribute to the extremely high rates of drug…
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.…
Maria Antony Dhivyan Je, Anoop Mn
Histone deacetylase (HDAC) and Histone acetyl-transferase (HAT) are enzymes that influence transcription by selectively deacetylating or acetylating the ε-amino groups of lysine located near the amino termini of core histone proteins. Over expression of HDACs noted in many forms of cancers including leukemia and breast…
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…
Janelle L. Lennie, John T. Mondick, Marc R. Gastonguay
Rare disease clinical trials are constrained to small sample sizes and may lack placebo-control, leading to challenges in drug development. This paper proposes a Bayesian model-based framework for early go/no-go decision making in rare disease drug development, using Duchenne muscular dystrophy (DMD) as an example.…
Christopher Southan
This report covers academic small-molecule drug development with a view to distilling guidelines. The first section covers research productivity feeding into commercial development before reviewing the literature on statistics of academic development It then considers differences between probes and drugs before…
Fabio Pammolli, Lorenzo Righetto, Sergio Abrignani, Luca Pani + 2 more
Analyses of pharmaceutical pipelines of drug development in the 1990-2010 documented progressively increasing attrition rates and duration of clinical trials, leading to a diffuse perception of a “productivity crisis”. We produced a new set of analyses for the last decade, using an extensive data of more than 45,000…
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…
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…
Chris Finan, Anna Gaulton, Felix. A Kruger, Tom Lumbers + 9 more
Target identification (identifying the correct drug targets for each disease) and target validation (demonstrating the effect of target perturbation on disease biomarkers and disease end-points) are essential steps in drug development. We showed previously that biomarker and disease endpoint associations of single…
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)…
Andreas M Svennebring, Jarl ES Wikberg
Three dedicated approaches to the calculation of the risk-adjusted net present value (rNPV) in drug discovery projects under different assumptions are suggested. The probability of finding a candidate drug suitable for clinical development and the time to the initiation of the clinical development is assumed to be…
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…
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…
Federica Riccio, Elisa Micarelli, Riccardo Secci, Giulio Giuliani + 5 more
Repurposing of drugs for new therapeutic use has received considerable attention for its potential to reduce time and cost of drug development. Here we present a new strategy to identify chemicals that are likely to induce differentiation of leukemic cells. As Acute Myeloid Leukemia (AML) is the result of a block in…
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…