116,703 research outputs found
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
Virtual screening, which identifies potential drugs from vast compound
databases to bind with a particular protein pocket, is a critical step in
AI-assisted drug discovery. Traditional docking methods are highly
time-consuming, and can only work with a restricted search library in real-life
applications. Recent supervised learning approaches using scoring functions for
binding-affinity prediction, although promising, have not yet surpassed docking
methods due to their strong dependency on limited data with reliable
binding-affinity labels. In this paper, we propose a novel contrastive learning
framework, DrugCLIP, by reformulating virtual screening as a dense retrieval
task and employing contrastive learning to align representations of binding
protein pockets and molecules from a large quantity of pairwise data without
explicit binding-affinity scores. We also introduce a biological-knowledge
inspired data augmentation strategy to learn better protein-molecule
representations. Extensive experiments show that DrugCLIP significantly
outperforms traditional docking and supervised learning methods on diverse
virtual screening benchmarks with highly reduced computation time, especially
in zero-shot setting
Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an
important in-silico tool for discovering new drugs in a faster and
cost-effective manner, especially for emerging diseases such as COVID-19. In
this paper, we propose a general-purpose framework combining a classical
Support Vector Classifier (SVC) algorithm with quantum kernel estimation for
LB-VS on real-world databases, and we argue in favor of its prospective quantum
advantage. Indeed, we heuristically prove that our quantum integrated workflow
can, at least in some relevant instances, provide a tangible advantage compared
to state-of-art classical algorithms operating on the same datasets, showing
strong dependence on target and features selection method. Finally, we test our
algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing
that hardware simulations provide results in line with the predicted
performances and can surpass classical equivalents.Comment: 16 pages, 7 figure
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction
Protein (receptor)--ligand interaction prediction is a critical component in
computer-aided drug design, significantly influencing molecular docking and
virtual screening processes. Despite the development of numerous scoring
functions in recent years, particularly those employing machine learning,
accurately and efficiently predicting binding affinities for protein--ligand
complexes remains a formidable challenge. Most contemporary methods are
tailored for specific tasks, such as binding affinity prediction, binding pose
prediction, or virtual screening, often failing to encompass all aspects. In
this study, we put forward DeepRLI, a novel protein--ligand interaction
prediction architecture. It encodes each protein--ligand complex into a fully
connected graph, retaining the integrity of the topological and spatial
structure, and leverages the improved graph transformer layers with cosine
envelope as the central module of the neural network, thus exhibiting superior
scoring power. In order to equip the model to generalize to conformations
beyond the confines of crystal structures and to adapt to molecular docking and
virtual screening tasks, we propose a multi-objective strategy, that is, the
model outputs three scores for scoring and ranking, docking, and screening, and
the training process optimizes these three objectives simultaneously. For the
latter two objectives, we augment the dataset through a docking procedure,
incorporate suitable physics-informed blocks and employ an effective
contrastive learning approach. Eventually, our model manifests a balanced
performance across scoring, ranking, docking, and screening, thereby
demonstrating its ability to handle a range of tasks. Overall, this research
contributes a multi-objective framework for universal protein--ligand
interaction prediction, augmenting the landscape of structure-based drug
design
Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network
Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response. A complementary approach is virtual screening, where chemical libraries can be efficiently screened against protein target(s). Here, we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis. We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer (MTC) characterized by a transformation network activated by oncogenic dRetM955T. Structural models for kinases relevant to MTC were generated for virtual screening to identify unique preliminary hits that suppressed dRetM955T-induced transformation. We then combined features from our hits with those of known inhibitors to create a ‘hybrid’ molecule with improved suppression of dRetM955T transformation. Our platform provides a framework to efficiently explore novel kinase inhibitors outside of explored inhibitor chemical space that are effective in inhibiting cancer networks while minimizing whole body toxicity
A Generic Framework and Methodology for Implementing Science Gateways for Analysing Molecular Docking Results
Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. This paper describes a generic framework and a related methodology that supports the efficient development of such environments. The framework is modular enabling the reuse of already existing components. The methodology is agile and encourages the input and participation of end-users. A prototype implementation, based on the framework and methodology, of a science-gateway-based molecular docking environment for recommending a ligand-protein pair for next docking experiment is also presented and evaluated
Design of a Simulation Tool for Audiology Education to provide Hearing Screening Training
Early identification of hearing impairment and ear disorders is important, which is why hearing screening is routinely done on newborns, with regular screening recommended on children through the age of 18. Screening is also completed with adults to assess and treat hearing problems. Procedural training is needed for new Speech-Language Pathologists and nursing students as well as continuing education for those trained to perform this procedure. An audiology simulator was developed to provide an alternative to traditional face-to-face lab instruction. Using a design science approach, the development of the simulation prototype is discussed. Contributions include a useful framework for developing such a simulation of an existing process, a description of a unique artifact that supports an individualized, self-paced learning environment using context-sensitive feedback and performance assessment, and an extensible approach to supporting virtual subjects in audiological training
AutoClickChem: Click Chemistry in Silico
Academic researchers and many in industry often lack the financial resources available to scientists working in “big pharma.” High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu
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