TY - JOUR AU - Bordukova, M. AU - Arneth, A.J.* AU - Makarov, N. AU - Brown, R.M.* AU - Schneider-Futschik, E.K.* AU - Dharmage, S.C.* AU - Ekinci, E.* AU - Crack, P.J.* AU - Hatters, D.M.* AU - Stewart, A.G.* AU - Stroud, D.* AU - Sadras, T.* AU - Anderson, G.P.* AU - Schmich, F.* AU - Rodriguez-Esteban, R.* AU - Menden, M.P. C1 - 74581 C2 - 57535 TI - Generative AI and Digital twins: Shaping a paradigm shift from precision to truly personalized medicine. JO - Expert Opin. Drug Discov. PY - 2025 SN - 1746-0441 ER - TY - JOUR AB - INTRODUCTION: The field of Natural Language Processing (NLP) within the life sciences has exploded in its capacity to aid the extraction and analysis of data from scientific texts in recent years through the advancement of Artificial Intelligence (AI). Drug discovery pipelines have been innovated and accelerated by the uptake of AI/Machine Learning (ML) techniques. AREAS COVERED: The authors provide background on Named Entity Recognition (NER) in text - from tagging terms in text using ontologies to entity identification via ML models. They also explore the use of Knowledge Graphs (KGs) in biological data ingestion, manipulation and extraction, leading into the modern age of Large Language Models (LLMs) and their ability to maneuver complex and abundant data. The authors also cover the main strengths and weaknesses of the many methods available when undertaking NLP tasks in drug discovery. Literature was derived from searches utilizing Europe PMC, ResearchRabbit and SciSpace. EXPERT OPINION: The mass of scientific data that is now produced each year is both a huge positive for potential innovation in drug discovery and a new hurdle for researchers to overcome. Notably, methods should be selected to fit a use case and the data available, as each method performs optimally under different conditions. AU - Withers, C.A.* AU - Rufai, A.M.* AU - Venkatesan, A.* AU - Tirunagari, S.* AU - Lobentanzer, S. AU - Harrison, M.* AU - Zdrazil, B.* C1 - 74219 C2 - 57398 CY - 2-4 Park Square, Milton Park, Abingdon Or14 4rn, Oxon, England SP - 765-783 TI - Natural language processing in drug discovery: Bridging the gap between text and therapeutics with artificial intelligence. JO - Expert Opin. Drug Discov. VL - 20 IS - 6 PB - Taylor & Francis Ltd PY - 2025 SN - 1746-0441 ER - TY - JOUR AB - INTRODUCTION: The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties. AREAS COVERED: The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials. EXPERT OPINION: The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients. AU - Bordukova, M. AU - Makarov, N. AU - Rodriguez-Esteban, R.* AU - Schmich, F.* AU - Menden, M.P. C1 - 68695 C2 - 54904 SP - 33-42 TI - Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. JO - Expert Opin. Drug Discov. VL - 19 IS - 1 PY - 2024 SN - 1746-0441 ER - TY - JOUR AB - INTRODUCTION: Collaborative computing has attracted great interest in the possibility of joining the efforts of researchers worldwide. Its relevance has further increased during the pandemic crisis since it allows for the strengthening of scientific collaborations while avoiding physical interactions. Thus, the E4C consortium presents the MEDIATE initiative which invited researchers to contribute via their virtual screening simulations that will be combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds will be tested, and the biological results will be shared with the scientific community. AREAS COVERED: In this paper, the MEDIATE initiative is described. This shares compounds' libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative's capacity to identify active compounds. EXPERT OPINION: Structure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files. AU - Vistoli, G.* AU - Manelfi, C.* AU - Talarico, C.* AU - Fava, A.* AU - Warshel, A.* AU - Tetko, I.V. AU - Apostolov, R.* AU - Ye, Y.* AU - Latini, C.* AU - Ficarelli, F.* AU - Palermo, G.* AU - Gadioli, D.* AU - Vitali, E.* AU - Varriale, G.* AU - Pisapia, V.* AU - Scaturro, M.* AU - Coletti, S.* AU - Gregori, D.* AU - Gruffat, D.* AU - Leija, E.* AU - Hessenauer, S.* AU - Delbianco, A.* AU - Allegretti, M.* AU - Beccari, A.R.* C1 - 68134 C2 - 54612 CY - 2-4 Park Square, Milton Park, Abingdon Or14 4rn, Oxon, England SP - 821-833 TI - MEDIATE - Molecular DockIng at homE: Turning collaborative simulations into therapeutic solutions. JO - Expert Opin. Drug Discov. VL - 18 IS - 8 PB - Taylor & Francis Ltd PY - 2023 SN - 1746-0441 ER - TY - JOUR AB - Precision medicine leverages molecular biomarkers for selecting optimal treatment strategies. Accordingly, stratifying patients into responder and non-responder has strongly accelerated drug discovery and drug approvals in the last two decades. Recently, the applications of artificial intelligence (AI) in healthcare have been promoting these processes, and continue to improve patient care through systematically analysing large-scale molecular data and electronic health records. In particular, preclinical pharmacogenomics data empowered AI to unfold its full potential.Areas covered: Here, we discuss the opportunities of AI in pharmacogenomics, drug discovery and precision medicine. In particular, we shed some light on the advancements in computational biomedicine from statistical, machine learning (ML) to complex deep learning (DL) models.Expert opinion: AI has already strongly impacted drug discovery, and will continue to revolutionise academic research and the pharmaceutical industry. Its algorithms aid the identification of novel treatment options through molecular signatures and thus pave the way for the next generation of precision medicine. AU - Farnoud, A. AU - Ohnmacht, A. AU - Meinel, M. AU - Menden, M.P. C1 - 65564 C2 - 52743 SP - 661-665 TI - Can artificial intelligence accelerate preclinical drug discovery and precision medicine? JO - Expert Opin. Drug Discov. VL - 17 IS - 7 PY - 2022 SN - 1746-0441 ER - TY - JOUR AB - Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice. AU - Boniolo, F AU - Dorigatti, E. AU - Ohnmacht, A. AU - Saur, D.* AU - Schubert, B. AU - Menden, M.P. C1 - 62209 C2 - 50728 CY - 2-4 Park Square, Milton Park, Abingdon Or14 4rn, Oxon, England SP - 991-1007 TI - Artificial intelligence in early drug discovery enabling precision medicine. JO - Expert Opin. Drug Discov. VL - 16 IS - 9 PB - Taylor & Francis Ltd PY - 2021 SN - 1746-0441 ER - TY - JOUR AB - INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. AREAS COVERED: In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening. EXPERT OPINION: Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine. AU - Baskin, I.I.* AU - Winkler, D.* AU - Tetko, I.V. C1 - 48930 C2 - 41512 CY - Abingdon SP - 785-795 TI - A renaissance of neural networks in drug discovery. JO - Expert Opin. Drug Discov. VL - 11 IS - 8 PB - Taylor & Francis Ltd PY - 2016 SN - 1746-0441 ER - TY - JOUR AB - Introduction: Historically, small-molecule drug discovery projects have largely focused on the G-protein-coupled receptor, ion-channel and enzyme target classes. More recently, there have been successes demonstrating that protein-protein interactions (PPIs) can be targeted by small-molecules and that this strategy has the potential to provide appropriate specificity and selectivity. However, a disadvantage is that compounds that modulate PPIs are often associated with relatively weak affinities as the targeted interaction surfaces are often relatively large. Moreover, from a small-molecule screening perspective, a large proportion of the initial screening Hits are often false positives and these need to be identified and excluded in order to focus on genuine modulators of the PPI being investigated. Areas covered: The authors review previous efforts on PPI modulator drug discovery. Furthermore, they review assays that can be employed in small-molecule screening and/or Hit validation. The PPI assays are categorized as: i) low-throughput target-based biochemical assays, which are primarily employed for Hit validation at the post-screening stage; ii) high-throughput target-based biochemical assays that are suitable for screening campaigns; and iii) cell-based assays, which are suitable for high-throughput screening campaigns and/or Hit validation. Expert opinion: Modulating the interaction of PPIs offers the potential to develop novel drugs to treat a wide range of diseases. New assay technologies are continually being developed and it is anticipated that these will be able to be directly used for small-molecule screening campaigns in the future. AU - Gul, S.* AU - Hadian, K. C1 - 42766 C2 - 35297 SP - 1393-1404 TI - Protein-protein interaction modulator drug discovery: Past efforts and future opportunities using a rich source of low- and high-throughput screening assays. JO - Expert Opin. Drug Discov. VL - 9 IS - 12 PY - 2014 SN - 1746-0441 ER - TY - JOUR AB - Introduction: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death worldwide. The growing burden of COPD is due to continuous tobacco use, which is the most important risk factor of the disease, indoor fumes, occupational exposures and also aging of the world's population. Epigenetic mechanisms significantly contribute to COPD pathophysiology. Areas covered: This review focuses on disease-relevant changes in DNA modification, histone modification and non-coding RNA expression in COPD, and provides insight into novel therapeutic approaches modulating epigenetic mechanisms. Recent findings revealed, among others, globally changed DNA methylation patterns, decreased levels of histone deacetylases and reduced microRNAs levels in COPD. The authors also discuss a potential role of the chromatin silencing Polycomb group of proteins in COPD. Expert opinion: COPD is a highly complex disease and therapy development is complicated by the fact that many smokers develop both COPD and lung cancer. Of interest, combination therapies involving DNA methyltransferase inhibitors and anti-inflammatory drugs provide a promising approach, as they might be therapeutic for both COPD and cancer. Although the field of epigenetic research has virtually exploded over the last 10 years, particular efforts are required to enhance our knowledge of the COPD epigenome in order to successfully establish epigenetic-based therapies for this widespread disease. AU - Schamberger, A.C. AU - Mise, N. AU - Meiners, S. AU - Eickelberg, O. C1 - 31360 C2 - 34444 CY - London SP - 609-628 TI - Epigenetic mechanisms in COPD: Implications for pathogenesis and drug discovery. JO - Expert Opin. Drug Discov. VL - 9 IS - 6 PB - Informa Healthcare PY - 2014 SN - 1746-0441 ER - TY - JOUR AB - Importance of the field: In recent years, proteomics has become a common technique applied to a wide spectrum of scientific problems, including the identification of diagnostic biomarkers, monitoring the effects of drug treatments or identification of chemical properties of a protein or a drug. Although being significantly different in scientific essence, the ultimate result of the majority of proteomics studies is a protein list. Thousands of independent proteomics studies have reported protein lists in various functional contexts. Areas covered in this review: We review here the spectrum of scientific problems where proteomics technology was applied recently to deliver protein lists. The available bioinformatics methods commonly used to understand the properties of the protein lists are compared. What the reader will gain: The types and common functional properties of the reported protein lists are discussed. The range of scientific problems where this knowledge could be potentially helpful with a focus on drug discovery issues is explored. Take home message: Reported protein lists represent a valuable resource which can be used for a variety of goals, ranging from biomarkers discovery to identification of novel therapeutic implications of known drugs. AU - Antonov, A.V. C1 - 5016 C2 - 28208 CY - London SP - 323-331 TI - Mining protein lists from proteomics studies: Applications for drug discovery. JO - Expert Opin. Drug Discov. VL - 5 IS - 4 PB - Informa Healthcare PY - 2010 SN - 1746-0441 ER -