1,721,176 research outputs found
Navigating the frontier of drug-like chemical space with cutting-edge generative AI models
No full textDeep generative models (GMs) have transformed the exploration of drug-like chemical space (CS) by generating novel molecules through complex, nontransparent processes, bypassing direct structural similarity. This review examines five key architectures for CS exploration: recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows (NF), and Transformers. It discusses molecular representation choices, training strategies for focused CS exploration, evaluation criteria for CS coverage, and related challenges. Future directions include refining models, exploring new notations, improving benchmarks, and enhancing interpretability to better understand biologically relevant molecular properties
Machine-learning approaches in drug discovery: methods and applications
No full textDuring the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and representative training-set compounds to learn robust decision rules. The explosive growth in the amount of public domain-available chemical and biological data has generated huge effort to design, analyze, and apply novel learning methodologies. Here, I focus on machine-learning techniques within the context of ligand-based VS (LBVS). In addition, I analyze several relevant VS studies from recent publications, providing a detailed view of the current state-of-the-art in this field and highlighting not only the problematic issues, but also the successes and opportunities for further advances
Deep learning in drug discovery: opportunities, challenges and future prospects
No full textArtificial Intelligence (AI) is an area of computer science that simulates the structures and operating principles of the human brain. Machine learning (ML) belongs to the area of AI and endeavors to develop models from exposure to training data. Deep Learning (DL) is another subset of AI, where models represent geometric transformations over many different layers. This technology has shown tremendous potential in areas such as computer vision, speech recognition and natural language processing. More recently, DL has also been successfully applied in drug discovery. Here, I analyze several relevant DL applications and case studies, providing a detailed view of the current state-of-the-art in drug discovery and highlighting not only the problematic issues, but also the successes and opportunities for further advances. This paper focuses on deep learning approaches in the context of drug discovery for designing new effective molecules, predicting for the desired molecular property profiles and planning synthesis
In Silico Drug Design and Discovery: Big Data for Small Molecule Design
No full text: Across life sciences, the steadily and rapidly increasing amount of data provide new opportunities for advancing knowledge and represent a key driver of emerging technological advancements [...]
Unleashing the power of generative AI in drug discovery
No full textArtificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development
Recent advances in developing PCSK9 inhibitors for lipid-lowering therapy
No full textCardiovascular disease is the major cause of death globally, with hypercholesterolemia being an important risk factor. The PCSK9 represents an attractive therapeutic target for hypercholesterolemia treatment and is currently in the spotlight of the scientific community. After autocatalytic activation in the hepatocyte endoplasmic reticulum, this convertase binds to the LDLR and channels it to the degradation pathway. This review gives an overview on the latest developments in the inhibition of PCSK9, including disruption of the protein-protein interaction (PPI) between PCSK9 and LDLR by peptidomimetics, adnectins and monoclonal antibodies and the suppression of PCSK9 expression by small molecules, siRNA and genome editing techniques. In addition, we discuss alternative approaches, such as anti-PCSK9 active vaccination and heparin mimetics
SYNTHESIS AND PHARMACOLOGICAL CHARACTERIZATION OF RIBOSE-MODIFIED ADENOSINE DERIVATIVES AS P1 RECEPTOR LIGANDS
No full textAdenosine, the natural ligand of P1 receptors, is implicated in the control of many physiological and pathological conditions such as inflammation, pain, cardiovascular and central nervous system (CNS) diseases.1
P1 receptors belong to the large family of GPCR receptors and are divided in four subtypes: A1, A2A, A2B and A3 adenosine receptors (ARs). Even though a large number of P1 ligands have been synthesized and characterized byin vitro and in vivo pharmacological studies, only very few of them are commercially available.
Modifications at the ribose moiety and substitution at the N6-position of adenosine, lead to adenosine derivatives endowed with increased potency at A1 or A3AR. Our previous SAR studies showed that the replacement of OH at 5’-position of the ribose moiety of N6-substituted adenosine derivatives by a chlorine improved A1AR potency and selectivity versus A3AR, with 5′-chloro-5′-deoxy-N6-(±)-(endo- norborn-2-yl)-adenosine (5′Cl5′d-(±)-ENBA) as one of the most potent and selective A1AR agonists,2 while a 5’-C-ethyl-tetrazolyl moiety maintained the A1AR potency, but restored high A3AR affinity, leading to very potent dual A1AR and A3AR ligands.3 Interestingly, both modifications at 5’-position of adenosine derivatives brought to human A3AR antagonism.
In order to further explore the structural determinants of this class of P1 ligands, a new series of ribose- modified N6-substituted adenosine derivatives was synthesized and their pharmacological profile was assayed. The results of this study will be discussed.References
1. Jacobson KA, Muller CE, Neuropharmacology 2015, doi: 10.1016/J.neuropharm.2015.12.001.
2. (a) Franchetti, P.; Cappellacci, L.; Vita, P.; Petrelli, R.; Lavecchia, A.; Kachler, S.; Klotz, K.-N.; Marabese, I.; Luongo, L.; Maione, S.; Grifantini, M. J. Med. Chem. 2009, 52, 2393−2406. (b) Luongo, L.; Petrelli, R.; Gatta, L.; Giordano, C.; Guida, F.; Vita, P.; Franchetti, P.; Grifantini, M.; De Novellis, V.; Cappellacci, L.; Maione, S. Molecules 2012, 17, 13712−13726. (c) Luongo, L.; Guida, F.; Imperatore, R.; Napolitano, F.; Gatta, L.; Cristino, L.; Giordano, C.; Siniscalco, D.; Di Marzo, V.; Bellini, G.; Petrelli, R.; Cappellacci, L.; Usiello, A.; de Novellis, V.; Rossi, F.; Maione, S. Glia 2014, 62, 122−132.
3. Petrelli, R.; Torquati, I.; Kachler, S.; Luongo, L.; Maione, S.; Franchetti, P.; Grifantini, M.; Novellino, E.; Lavecchia, A.; Klotz, K.-N-; Cappellacci, L. J. Med. Chem. 2015, 58, 2560-2566
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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