Populating Chemical Space with Peptides Using a Genetic Algorithm

Capecchi, Alice; Zhang, Alain; Reymond, Jean‐Louis

doi:10.1021/acs.jcim.9b01014

Cited by 25 publications

(34 citation statements)

References 72 publications

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“…Third, all atom-pair shingles ( )| , | ( ) are written for each atom pair ( , ) and each value of , placing the two SMILES strings ( ) and ( ) in lexicographical order ( Figure 1). Fourth, the resulting set of atom-pair shingles is hashed to a set of integers using the unique mapping SHA-1, [39] and its corresponding transposed vector is finally MinHashed to form the MAP4 vector (Equation 1). A detailed description of the MinHash method used here can be found in our recent publication on MHFP6.…”

Section: Fingerprint Calculationmentioning

confidence: 99%

“…We as a good performing fingerprint for large molecules and peptides, [27,28] We use five different metrics in the benchmark, namely AUC (Figure 2a), RIE100…”

Section: Benchmarking Study Designmentioning

confidence: 99%

“…[21] We also used atom-pair fingerprints to discover and optimize novel antimicrobial peptides in virtual libraries of bicyclic peptides [22,23] and peptide dendrimers, [24,25] to create chemical space maps [26] of molecules beyond the Lipinski limit found in the PubChem and ChEMBL databases, [27] and to drive a genetic algorithm to produce analogs of peptides with diverse chain topologies. [28] Overall, atom-pair fingerprints have an excellent perception of molecular shape for both large and small molecules and overcome the above-mentioned limitations. However, they do not encode molecular structure in detail and perform poorly in small molecule benchmarking studies compared to substructure fingerprints such as ECFP4 and…”

Section: Introductionmentioning

confidence: 99%

“…The peptide active and inactive datasets and the training lists can be found at https://github.com/reymond-group/map4. from the SwissProt dataset and translated into non-chiral SMILES using PDGA,[28] resulting in 9,054 unique structures.…”

mentioning

confidence: 99%

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One Molecular Fingerprint to Rule them All: Drugs, Biomolecules, and the Metabolome

Capecchi¹,

Probst²,

Reymond³

2020

Preprint

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Background: Molecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules. Results: Here we set out to design a new fingerprint suitable for both small and large molecules by combining substructure and atom-pair concepts. Our quest resulted in a new fingerprint called MinHashed atom-pair fingerprint up to a diameter of four bonds (MAP4). In this fingerprint the circular substructures with radii of r = 1 and r = 2 bonds around each atom in an atom-pair are written as two pairs of SMILES, each pair being combined with the topological distance separating the two central atoms. These so-called atom-pair molecular shingles are hashed, and the resulting set of hashes is MinHashed to form the MAP4 fingerprint. MAP4 significantly outperforms all other fingerprints on an extended benchmark that combines the Riniker and Landrum small molecule benchmark with a peptide benchmark recovering BLAST analogs from either scrambled or point mutation analogs. MAP4 furthermore produces well-organized chemical space tree-maps (TMAPs) for databases as diverse as DrugBank, ChEMBL, SwissProt and the Human Metabolome Database (HMBD), and differentiates between all metabolites in HMBD, over 70 % of which are indistinguishable from their nearest neighbor using substructure fingerprints. Conclusion: MAP4 is a new molecular fingerprint suitable for drugs, biomolecules, and the metabolome and can be adopted as a universal fingerprint to describe and search chemical space. The source code is available at <a href="https://github.com/reymond-group/map4">https://github.com/reymond-group/map4</a> and interactive MAP4 similarity search tools and TMAPs for various databases are accessible at <a href="http://map-search.gdb.tools/">http://map-search.gdb.tools/</a> and <a href="http://tm.gdb.tools/map4/">http://tm.gdb.tools/map4/</a>.<a href="http://tm.gdb.tools/map4/"></a>

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Section: Fingerprint Calculationmentioning

confidence: 99%

“…We as a good performing fingerprint for large molecules and peptides, [27,28] We use five different metrics in the benchmark, namely AUC (Figure 2a), RIE100…”

Section: Benchmarking Study Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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One Molecular Fingerprint to Rule them All: Drugs, Biomolecules, and the Metabolome

Capecchi¹,

Probst²,

Reymond³

2020

Preprint

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“…Combinatorial libraries expand chemical space contributing to the identification of active peptides, thus being a useful tool in drug discovery [15,16] . Recently, techniques such as genetic learning algorithms are used to generate peptide analogs similar to a compound of interest [17] …”

Section: Introductionmentioning

confidence: 99%

D‐Peptide Builder: A Web Service to Enumerate, Analyze, and Visualize the Chemical Space of Combinatorial Peptide Libraries

Díaz-Eufracio

Palomino-Hernández

Arredondo-Sánchez

et al. 2020

Molecular Informatics

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Peptide‐based drug discovery is re‐gaining attention in drug discovery. Similarly, combinatorial chemistry continues to be a useful technique for the rapid exploration of chemical space. A current challenge, however, is the enumeration of combinatorial peptide libraries using freely accessible tools. To facilitate the swift enumeration of combinatorial peptide libraries, we introduce herein D‐Peptide Builder. In the current version, the user can build up to pentapeptides, linear or cyclic, using the natural pool of 20 amino acids. The user can use non‐ and/or N‐methylated amino acids. The server also enables the rapid visualization of the chemical space of the newly enumerated peptides in comparison with other libraries relevant to drug discovery and preloaded in the server. D‐Peptide Builder is freely accessible at http://dpeptidebuilder. quimica.unam.mx:4000/. It is also accessible through the open D‐Tools platform (DIFACQUIM Tools for Chemoinformatics https://www.difacquim.com/d‐tools/).

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Machine Learning Guided Discovery of Non‐Hemolytic Membrane Disruptive Anticancer Peptides

et al. 2022

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Most antimicrobial peptides (AMPs) and anticancer peptides (ACPs) fold into membrane disruptive cationic amphiphilic αhelices, many of which are however also unpredictably hemolytic and toxic. Here we exploited the ability of recurrent neural networks (RNN) to distinguish active from inactive and non-hemolytic from hemolytic AMPs and ACPs to discover new non-hemolytic ACPs. Our discovery pipeline involved: 1) sequence generation using either a generative RNN or a genetic algorithm, 2) RNN classification for activity and hemolysis, 3) selection for sequence novelty, helicity and amphiphilicity, and 4) synthesis and testing. Experimental evaluation of thirty-three peptides resulted in eleven active ACPs, four of which were non-hemolytic, with properties resembling those of the natural ACP lasioglossin III. These experiments show the first example of direct machine learning guided discovery of non-hemolytic ACPs.

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Populating Chemical Space with Peptides Using a Genetic Algorithm

Cited by 25 publications

References 72 publications

One Molecular Fingerprint to Rule them All: Drugs, Biomolecules, and the Metabolome

One Molecular Fingerprint to Rule them All: Drugs, Biomolecules, and the Metabolome

D‐Peptide Builder: A Web Service to Enumerate, Analyze, and Visualize the Chemical Space of Combinatorial Peptide Libraries

Machine Learning Guided Discovery of Non‐Hemolytic Membrane Disruptive Anticancer Peptides

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