scholarly journals Retro Drug Design: From Target Properties to Molecular Structures

2021 ◽  
Author(s):  
Yuhong Wang ◽  
Sam Michael ◽  
Ruili Huang ◽  
Jinghua Zhao ◽  
Katlin Recabo ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Molecular Descriptor ◽  
Pharmaceutical Research ◽  
Monte Carlo Sampling ◽  
Molecular Structures ◽  
Descriptor System ◽  
Chemical Structures ◽  
Drug Molecules ◽  
Property Space

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate μ opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.

scholarly journals Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

2021 ◽  
Vol 14 (10) ◽  
pp. 1062
Author(s):  
Tomasz Róg ◽  
Mykhailo Girych ◽  
Alex Bunker
Keyword(s):  
Molecular Dynamics ◽  
Drug Design ◽  
Active Site ◽  
Lipid Membranes ◽  
Md Simulation ◽  
Lipid Membrane ◽  
Pharmaceutical Research ◽  
Specific Protein ◽  
Design Tool ◽  
Drug Molecules

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard “lock and key” paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

scholarly journals Comprehensive Survey of Recent Drug Discovery Using Deep Learning

2021 ◽  
Vol 22 (18) ◽  
pp. 9983
Author(s):  
Jintae Kim ◽  
Sera Park ◽  
Dongbo Min ◽  
Wankyu Kim
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Molecular Structures ◽  
De Novo Drug Design ◽  
Related Data ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Model Training

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug–target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

scholarly journals Nuclear Magnetic Resonance Spectroscopyan Evolutionary Approach to Drug Design

2007 ◽  
Vol 4 (3) ◽  
pp. 294-301
Author(s):  
Neeraj Upmanyu ◽  
Gopal Garg ◽  
Archana Dolly ◽  
Pradeep Mishra
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Drug Discovery ◽  
Magnetic Resonance ◽  
Drug Design ◽  
Small Molecules ◽  
Pharmaceutical Research ◽  
Three Dimensional ◽  
Drug Discovery And Development ◽  
4D Nmr ◽  
Nuclear Magnetic

Ever since Nuclear Magnetic Resonance (NMR) spectroscopy hit the analytical scene; its capabilities and applications continue to evolve. Originally designed as a way to verify the structure of relatively small compounds, the technology of NMR has exploded and become a valuable means for studying protein structure. NMR has proved to be a valuable tool in pharmaceutical research, as it has entered new arena of drug discovery and structural genomics. NMR can provide information on the three-dimensional structures of small molecules in solution, high-molecular-weight complexes, and the details of enzyme mechanisms that can be used to aid in drug design. In the present scenario, the availability of high magnetic fields; improved software, high resolution probes, and electronics; more versatile pulse programmers; and most importantly the development of 2D, 3D and 4D NMR, have revolutionized the field of drug discovery and development.

scholarly journals DrugSpaceX: a large screenable and synthetically tractable database extending drug space

2020 ◽  
Vol 49 (D1) ◽  
pp. D1170-D1178
Author(s):  
Tianbiao Yang ◽  
Zhaojun Li ◽  
Yingjia Chen ◽  
Dan Feng ◽  
Guangchao Wang ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Chemical Space ◽  
Three Dimensional ◽  
Chemical Properties ◽  
Biological Properties ◽  
Chemical Structures ◽  
Drug Molecules ◽  
Ligand Identification ◽  
Strategy Planning ◽  
Efficient Exploration

Abstract One of the most prominent topics in drug discovery is efficient exploration of the vast drug-like chemical space to find synthesizable and novel chemical structures with desired biological properties. To address this challenge, we created the DrugSpaceX (https://drugspacex.simm.ac.cn/) database based on expert-defined transformations of approved drug molecules. The current version of DrugSpaceX contains >100 million transformed chemical products for virtual screening, with outstanding characteristics in terms of structural novelty, diversity and large three-dimensional chemical space coverage. To illustrate its practical application in drug discovery, we used a case study of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, to show DrugSpaceX performing a quick search of initial hit compounds. Additionally, for ligand identification and optimization purposes, DrugSpaceX also provides several subsets for download, including a 10% diversity subset, an extended drug-like subset, a drug-like subset, a lead-like subset, and a fragment-like subset. In addition to chemical properties and transformation instructions, DrugSpaceX can locate the position of transformation, which will enable medicinal chemists to easily integrate strategy planning and protection design.

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

Current Scenario in Structure and Ligand-Based Drug Design on Anti-colon Cancer Drugs

2019 ◽  
Vol 24 (32) ◽  
pp. 3829-3841 ◽  
Author(s):  
Lakshmanan Loganathan ◽  
Karthikeyan Muthusamy
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Anticancer Agent ◽  
Computational Power ◽  
Cancer Drugs ◽  
Recent Advances ◽  
Anti Cancer ◽  
Drug Designing ◽  
Current Scenario ◽  
Molecular Modeling Studies

Worldwide, colorectal cancer takes up the third position in commonly detected cancer and fourth in cancer mortality. Recent progress in molecular modeling studies has led to significant success in drug discovery using structure and ligand-based methods. This study highlights aspects of the anticancer drug design. The structure and ligand-based drug design are discussed to investigate the molecular and quantum mechanics in anti-cancer drugs. Recent advances in anticancer agent identification driven by structural and molecular insights are presented. As a result, the recent advances in the field and the current scenario in drug designing of cancer drugs are discussed. This review provides information on how cancer drugs were formulated and identified using computational power by the drug discovery society.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

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