Artificial Intelligence in Drug Design

Heßler, Gerhard; Baringhaus, Karl‐Heinz

doi:10.3390/molecules23102520

Cited by 285 publications

(187 citation statements)

References 74 publications

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“…In recent years, a more data‐hungry ML algorithm, deep learning (DL), which has gained great success in a wide variety of applications, such as computer vision, speech recognition, computer games, and natural language processing, has also attracted considerable interest from computational chemists and medicinal chemists. Up to now, various reviews related to the applications of ML or DL in drug design and discovery have been published . Ain et al and Khamis et al summarized the advances of ML‐based SFs before 2015 in two comprehensive reviews about protein–ligand binding affinity prediction and SBVS, but DL has just begun to rise in the field of drug discovery in 2015 .…”

Section: Introductionmentioning

confidence: 99%

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Shen

Ding

Wang

et al. 2019

WIREs Comput Mol Sci

197

180

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Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy highly depends on the reliability of scoring functions (SFs). With the rapid development of machine learning (ML) techniques, ML-based SFs have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and virtual screening, and most of them have shown significantly better performance than a wide range of classical SFs. Emergence of more data-hungry deep learning (DL) approaches in recent years further fascinates the exploitation of more accurate SFs. Here, we summarize the progress of traditional ML-based SFs in the last few years and provide insights into recently developed DL-based SFs. We believe that the continuous improvement in ML-based SFs can surely guide the early-stage drug design and accelerate the discovery of new drugs. This article is categorized under:Computer and Information Science > Chemoinformaticsdeep learning, machine learning, molecular docking, scoring function, structure-based drug design | INTRODUCTIONTraditional drug discovery largely relies on the application of high-throughput screening, an experimental technique with acceptable performance but high cost and low efficiency. 1 With the rapid development of computational chemistry and computer technology, computer-aided drug design (CADD) has gradually emerged as a powerful technique in the design and development of new drug candidates in the past three decades. 2 Virtual screening (VS), an important branch of CADD, can enrich potential actives from large virtual compound libraries through in silico methods rather than real experiments, which can not only accelerate the process of drug discovery but also greatly reduce the time and resource cost. 3-5 Depending on whether the three-dimensional (3D) structure of a target is used or not, VS approaches can be classified into two major categories: ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS). 6 LBVS aims to discover active molecules through the models developed based on a set of known ligands of a target of interest, which may limit its capability to find novel chemotypes. Compared with LBVS, SBVS is considered to be a better choice to discover novel active compounds if the 3D structure of a given target is available. 7 Chao Shen and Junjie Ding are equivalent first authors.

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Section: Introductionmentioning

confidence: 99%

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Shen

Ding

Wang

et al. 2019

WIREs Comput Mol Sci

197

180

View full text Add to dashboard Cite

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“…The collection of methods enabling computational de novo design has recently been expanded by techniques utilizing machine learning, rather than explicit chemical transformations, for the construction of potentially novel molecules . Certain classes of artificial neural networks, especially recurrent neural networks (RNN), are particularly suitable for this purpose .…”

Section: Introductionmentioning

confidence: 99%

Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

et al. 2019

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Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell‐migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top‐scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.

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“…Effectively, this opens doors for predictive toxicology (see Figure ). By providing images, molecular graphs, 3D grids, or SMILES strings, the network can learn the necessary properties or patterns by itself, based on the assumption that all information needed is encoded in the structure . Especially in the case of graphs, smiles or 3D grids, the model is not biased by the user's selection of descriptors, which might not be suitable for the task at hand.…”

Section: Machine Learning Based Predictionsmentioning

confidence: 99%

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

Hemmerich

Ecker

2020

WIREs Comput Mol Sci

121

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In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics

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Artificial Intelligence in Drug Design

Cited by 285 publications

References 74 publications

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

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