Recent applications of machine learning in medicinal chemistry

Panteleev, Jane; Gao, Hua; Jia, Lei

doi:10.1016/j.bmcl.2018.06.046

Cited by 102 publications

(81 citation statements)

References 74 publications

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“…158 Since then, the development of better reward functions has greatly helped to mitigate such issues, but low diversity and novelty remains an issue. [159][160][161] After reviewing the work that has been done so far on reward function design, we conclude that good reward functions should lead to generated molecules which meet the following desiderata:…”

Section: Reward Function Designmentioning

confidence: 99%

Deep learning for molecular design—a review of the state of the art

Elton

Boukouvalas

Fuge

et al. 2019

Mol. Syst. Des. Eng.

536

516

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In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text. Inspired by these successes, researchers are now applying deep generative modeling techniques to the generation and optimization of molecules-in our review we found 45 papers on the subject published in the past two years. These works point to a future where such systems will be used to generate lead molecules, greatly reducing resources spent downstream synthesizing and characterizing bad leads in the lab. In this review we survey the increasingly complex landscape of models and representation schemes that have been proposed. The four classes of techniques we describe are recursive neural networks, autoencoders, generative adversarial networks, and reinforcement learning. After first discussing some of the mathematical fundamentals of each technique, we draw high level connections and comparisons with other techniques and expose the pros and cons of each. Several important high level themes emerge as a result of this work, including the shift away from the SMILES string representation of molecules towards more sophisticated representations such as graph grammars and 3D representations, the importance of reward function design, the need for better standards for benchmarking and testing, and the benefits of adversarial training and reinforcement learning over maximum likelihood based training.

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Section: Reward Function Designmentioning

confidence: 99%

Deep learning for molecular design—a review of the state of the art

Elton

Boukouvalas

Fuge

et al. 2019

Mol. Syst. Des. Eng.

536

516

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“…The development of pyrazolone derivatives can be regarded as the epitome of medicinal chemistry, starting from antipyrine, several analogues have been explored and the precursor designing methods have evolved from structural modification to Fragment Based Drug Design and high-throughput screening simultaneously. With the development of science, machine learning, virtual screening and combinatorial chemistry technologies shorten the time of filtrating the leading compounds and will play a more and more important role in drug design in the future [144,145]. Investigations for privileged scaffold from the perspective of SAR have been lasting several years [146e151].…”

Section: Conclusion and Perspectivementioning

confidence: 99%

Pyrazolone structural motif in medicinal chemistry: Retrospect and prospect

Zhao

Dai

et al. 2020

European Journal of Medicinal Chemistry

136

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a b s t r a c tThe pyrazolone structural motif is a critical element of drugs aimed at different biological end-points. Medicinal chemistry researches have synthesized drug-like pyrazolone candidates with several medicinal features including antimicrobial, antitumor, CNS (central nervous system) effect, anti-inflammatory activities and so on. Meanwhile, SAR (Structure-Activity Relationship) investigations have drawn attentions among medicinal chemists, along with a plenty of analogues have been derived for multiple targets. In this review, we comprehensively summarize the biological activity and SAR for pyrazolone analogues, wishing to give an overall retrospect and prospect on the pyrazolone derivatives.

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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

186

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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Recent applications of machine learning in medicinal chemistry

Cited by 102 publications

References 74 publications

Deep learning for molecular design—a review of the state of the art

Deep learning for molecular design—a review of the state of the art

Pyrazolone structural motif in medicinal chemistry: Retrospect and prospect

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

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