rnaimehaom Goto Github PK

log_p_prediction

logd74

A high-quality hand-curated logD7.4 dataset of 1,130 compounds

logexpert

Windows tail program and log file analyzer.

low-data-generative-models

lpsolve

R Interface to 'Lp_solve' v. 5.5 to Solve Linear/Integer Programs

lsa

LSA: a local-weighted structure alignment tool for pharmaceutical virtual screening

lsc

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

lshr

Locality Sensitive Hashing In R

lstm_chem

Implementation of the paper - Generative Recurrent Networks for De Novo Drug Design.

lstm_druganaloggeneration

lstm_peptides

Long short-term memory recurrent neural networks for learning peptide and protein sequences to later design new, similar examples.

luca

Single-cell Lung Cancer Atlas with 1.2M cells

luna

lund

Some code related to "Learning by Unsupervised Nonlinear Diffusion" by Maggioni and Murphy (JMLR 2020).

lychi

Layered Chemical Identifier

m2p

macaw

machine-learning---chem-molecules-generation-from-smiles-dataset

machine-learning-aided-retrosynthesis

Retrosynthesis has been used for a long time, but it's slower and prone to bias. Machine learning aided techniques like this can help speed up the process.

machine-learning-collection

A resource for learning about Machine learning & Deep Learning

machine-learning-for-photovoltaic-material-property-prediction

Multi-layer perceptron model for photo-voltaic material properties prediction.

machine-learning-for-proteins

Listing of papers about machine learning for proteins.

machine-learning-for-thermoelectrics-discovery

Transition metal oxides are attractive materials for high temperature thermoelectric applications due to their thermal stability, low cost bulk processing and natural abundance. Notwithstanding the high power factor, their high thermal conductivity is a roadblock in achieving higher efficiency. The search space for new thermoelectric oxides has been limited to the alloys of a few previously explored systems, such as ZnO, SrTiO3 and CaMnO3. The phenomenon of thermal conduction in crystalline alloys and its dependence on crystal properties is also poorly understood, which limits the ability to design new alloys. In this paper, we apply machine-learning models for discovering novel transition metal oxides with low lattice thermal conductivity (kL). A two-step process is proposed to address the problem of small datasets frequently encountered in materials informatics. First, a gradient boosted tree classifier is learnt to categorize unknown compounds into three categories of thermal conductivity: Low, Medium, and High. In the second step, we fit regression models on the targeted class (i.e. low kL) to estimate kL with an R2 value of 0.96. Gradient boosted tree model was also used to identify key material properties influencing classification of kL, namely lattice energy per atom, atom density, electronic energy band gap, mass density, and ratio of oxygen by transition metal atoms. Only fundamental materials properties describing the crystal symmetry, compound chemistry and interatomic bonding were used in the classification process, which can be readily used as selection parameters. The proposed two-step process addresses the problem of small datasets and improves the predictive accuracy.

machine-learning-interview

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

machine-learning-meets-pka

machine-learning-on-drug-solubility

Using multiple machine learning algorithms to generate the best predicitive model for the degree of solubility of a chemical compound given its molecular formula.

machine-learning-project-4

Aqueous solubility prediction

machine_learning_pka

Python based feed forward artificial neural neteork model for predicting pka of drug-like molecules from chembl database

machinelearning

Machine learning and artificial intelligence

macro_analyzer

This is a public source for the files reported in "Automatization of Generic Shapes for Macrocycle Conformational Sampling"